diff --git a/hutool-core/src/main/java/cn/hutool/core/thread/RecyclableBatchThreadPoolExecutor.java b/hutool-core/src/main/java/cn/hutool/core/thread/RecyclableBatchThreadPoolExecutor.java
new file mode 100644
index 000000000..97b55ccbc
--- /dev/null
+++ b/hutool-core/src/main/java/cn/hutool/core/thread/RecyclableBatchThreadPoolExecutor.java
@@ -0,0 +1,303 @@
+package cn.hutool.core.thread;
+
+import java.util.*;
+import java.util.concurrent.*;
+import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.function.Function;
+import java.util.function.Supplier;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+
+/**
+ * 可召回批处理线程池执行器
+ * <pre>
+ * 1.数据分批并行处理
+ * 2.主线程、线程池混合执行批处理任务，主线程空闲时会尝试召回线程池队列中的任务执行
+ * 3.线程安全，可用同时执行多个任务，线程池满载时，效率与单线程模式相当，无阻塞风险，无脑提交任务即可
+ * </pre>
+ *
+ * 适用场景：
+ * <pre>
+ * 1.批量处理数据且需要同步结束的场景，能一定程度上提高吞吐量、防止任务堆积 {@link #process(List, int, Function)}
+ * 2.普通查询接口加速 {@link #processByWarp(Warp[])}
+ * </pre>
+ *
+ * @author likuan
+ */
+public class RecyclableBatchThreadPoolExecutor {
+
+	private final ExecutorService executor;
+
+	public RecyclableBatchThreadPoolExecutor(int poolSize){
+		this(poolSize,"recyclable-batch-pool-");
+	}
+
+	/**
+	 * 建议的构造方法
+	 * <pre>
+	 * 1.使用无界队列，主线程会召回队列中的任务执行，不会有任务堆积，无需考虑拒绝策略
+	 * 2.假如在web场景中请求量过大导致oom，不使用此工具也会有同样的结果，甚至更严重，应该对请求做限制或做其他优化
+	 * </pre>
+	 *
+	 * @param poolSize 线程池大小
+	 * @param threadPoolPrefix 线程名前缀
+	 */
+	public RecyclableBatchThreadPoolExecutor(int poolSize, String threadPoolPrefix){
+		AtomicInteger threadNumber = new AtomicInteger(1);
+		ThreadFactory threadFactory = r -> {
+			Thread t = new Thread(r, threadPoolPrefix + threadNumber.getAndIncrement());
+			if (t.isDaemon()) {
+				t.setDaemon(false);
+			}
+			if (t.getPriority() != Thread.NORM_PRIORITY) {
+				t.setPriority(Thread.NORM_PRIORITY);
+			}
+			return t;
+		};
+		this.executor = new ThreadPoolExecutor(poolSize, poolSize, 0L, TimeUnit.MILLISECONDS, new LinkedBlockingQueue<>(),threadFactory);
+	}
+
+	/**
+	 * 自定义线程池，一般不需要使用
+	 * @param executor 线程池
+	 */
+	public RecyclableBatchThreadPoolExecutor(ExecutorService executor){
+		this.executor = executor;
+	}
+
+	/**
+	 * 关闭线程池
+	 */
+	public void shutdown(){
+		executor.shutdown();
+	}
+
+	/**
+	 * 获取线程池
+	 * @return ExecutorService
+	 */
+	public ExecutorService getExecutor(){
+		return executor;
+	}
+
+	/**
+	 * 分批次处理数据
+	 * <pre>
+	 * 1.所有批次执行完成后会过滤null并返回合并结果，保持输入数据顺序，不需要结果{@link Function}返回null即可
+	 * 2.{@link Function}需自行处理异常、保证线程安全
+	 * 3.原始数据在分片后可能被外部修改，导致批次数据不一致，如有必要，传参之前进行数据拷贝
+	 * 4.主线程会参与处理批次数据，如果要异步执行任务请使用普通线程池
+	 * </pre>
+	 *
+	 * @param <T> 输入数据类型
+	 * @param <R> 输出数据类型
+	 * @param data 待处理数据集合
+	 * @param batchSize 每批次数据量
+	 * @param processor 单条数据处理函数
+	 * @return 处理结果集合
+	 */
+	public <T,R> List<R> process(List<T> data, int batchSize, Function<T,R> processor) {
+		if (batchSize < 1) {
+			throw new IllegalArgumentException("batchSize >= 1");
+		}
+		List<List<T>> batches = splitData(data, batchSize);
+		int batchCount = batches.size();
+		int minusOne = batchCount - 1;
+		ArrayDeque<IdempotentTask<R>> taskQueue = new ArrayDeque<>(minusOne);
+		Map<Integer,Future<TaskResult<R>>> futuresMap = new HashMap<>();
+		// 提交前 batchCount-1 批任务
+		for (int i = 0 ; i < minusOne ; i++) {
+			final int index = i;
+			IdempotentTask<R> task = new IdempotentTask<>(i,() -> processBatch(batches.get(index), processor));
+			taskQueue.add(task);
+			futuresMap.put(i,executor.submit(task));
+		}
+		@SuppressWarnings("unchecked")
+		List<R>[] resultArr = new ArrayList[batchCount];
+		// 处理最后一批
+		resultArr[minusOne] = processBatch(batches.get(minusOne), processor);
+		// 处理剩余任务
+		processRemainingTasks(taskQueue, futuresMap,resultArr);
+		//排序、过滤null
+		return Stream.of(resultArr)
+				.filter(Objects::nonNull)
+				.flatMap(List::stream)
+				.collect(Collectors.toList());
+	}
+
+	/**
+	 * 处理剩余任务并收集结果
+	 * @param taskQueue 任务队列
+	 * @param futuresMap 异步任务映射
+	 * @param resultArr 结果存储数组
+	 */
+	private <R> void processRemainingTasks(Queue<IdempotentTask<R>> taskQueue, Map<Integer,Future<TaskResult<R>>> futuresMap, List<R>[] resultArr) {
+		// 主消费未执行任务
+		IdempotentTask<R> task;
+		while ((task = taskQueue.poll()) != null) {
+			try {
+				TaskResult<R> call = task.call();
+				if (call.effective) {
+					// 取消被主线程执行任务
+					Future<TaskResult<R>> future = futuresMap.remove(task.index);
+					future.cancel(false);
+					//加入结果集
+					resultArr[task.index] = call.result;
+				}
+			} catch (Exception e) {
+				// 不处理异常
+				throw new RuntimeException(e);
+			}
+		}
+		futuresMap.forEach((index,future)->{
+			try {
+				TaskResult<R> taskResult = future.get();
+				if(taskResult.effective){
+					resultArr[index] = taskResult.result;
+				}
+			} catch (InterruptedException | ExecutionException e) {
+				throw new RuntimeException(e);
+			}
+		});
+	}
+
+	/**
+	 * 幂等任务包装类，确保任务只执行一次
+	 */
+	private static class IdempotentTask<R> implements Callable<TaskResult<R>> {
+
+		private final int index;
+		private final Callable<List<R>> delegate;
+		private final AtomicBoolean executed = new AtomicBoolean(false);
+
+		IdempotentTask(int index,Callable<List<R>> delegate) {
+			this.index = index;
+			this.delegate = delegate;
+		}
+
+		@Override
+		public TaskResult<R> call() throws Exception {
+			if (executed.compareAndSet(false, true)) {
+				return new TaskResult<>(delegate.call(), true);
+			}
+			return new TaskResult<>(null, false);
+		}
+	}
+
+	/**
+	 * 结果包装类，标记结果有效性
+	 */
+	private static class TaskResult<R>{
+		private final List<R> result;
+		private final boolean effective;
+		TaskResult(List<R> result, boolean effective){
+			this.result = result;
+			this.effective = effective;
+		}
+	}
+
+	/**
+	 * 数据分片方法
+	 * @param data 原始数据
+	 * @param batchSize 每批次数据量
+	 * @return 分片后的二维集合
+	 */
+	private static <T> List<List<T>> splitData(List<T> data, int batchSize) {
+		int batchCount = (data.size() + batchSize - 1) / batchSize;
+		return new AbstractList<List<T>>() {
+			@Override
+			public List<T> get(int index) {
+				int from = index * batchSize;
+				int to = Math.min((index + 1) * batchSize, data.size());
+				return data.subList(from, to);
+			}
+
+			@Override
+			public int size() {
+				return batchCount;
+			}
+		};
+	}
+
+	/**
+	 * 单批次数据处理
+	 * @param batch 单批次数据
+	 * @param processor 处理函数
+	 * @return 处理结果
+	 */
+	private static <T,R> List<R> processBatch(List<T> batch, Function<T,R> processor) {
+		return batch.stream().map(processor).filter(Objects::nonNull).collect(Collectors.toList());
+	}
+
+	/**
+	 * 处理Warp数组
+	 *
+	 * <pre>{@code
+	 * Warp<String> warp1 = Warp.of(this::select1);
+	 * Warp<List<String>> warp2 = Warp.of(this::select2);
+	 * executor.processByWarp(warp1, warp2);
+	 * String r1 = warp1.get();
+	 * List<String> r2 = warp2.get();
+	 * }</pre>
+	 *
+	 * @param warps Warp数组
+	 * @return Warp集合,此方法返回结果为空的不会被过滤
+	 */
+	public List<Warp<?>> processByWarp(Warp<?>... warps) {
+		return processByWarp(Arrays.asList(warps));
+	}
+
+	/**
+	 * 处理Warp集合
+	 * @param warps Warp集合
+	 * @return Warp集合,此方法返回结果为空的不会被过滤
+	 */
+	public List<Warp<?>> processByWarp(List<Warp<?>> warps) {
+		return process(warps, 1, Warp::execute);
+	}
+
+	/**
+	 * 处理逻辑包装类
+	 * @param <R> 结果类型
+	 */
+	public static class Warp<R>{
+
+		private Warp(Supplier<R> supplier){
+			Objects.requireNonNull(supplier);
+			this.supplier = supplier;
+		}
+
+		/**
+		 * 创建Warp
+		 * @param supplier 执行逻辑
+		 * @return Warp
+		 * @param <R> 结果类型
+		 */
+		public static <R> Warp<R> of(Supplier<R> supplier){
+			return new Warp<>(supplier);
+		}
+
+		private final Supplier<R> supplier;
+		private R result;
+
+		/**
+		 * 获取结果
+		 * @return 结果
+		 */
+		public R get() {
+			return result;
+		}
+
+		/**
+		 * 执行
+		 * @return this
+		 */
+		public Warp<R> execute() {
+			result = supplier.get();
+			return this;
+		}
+
+	}
+
+}
diff --git a/hutool-core/src/test/java/cn/hutool/core/thread/RecyclableBatchThreadPoolExecutorTest.java b/hutool-core/src/test/java/cn/hutool/core/thread/RecyclableBatchThreadPoolExecutorTest.java
new file mode 100644
index 000000000..d8dc2f182
--- /dev/null
+++ b/hutool-core/src/test/java/cn/hutool/core/thread/RecyclableBatchThreadPoolExecutorTest.java
@@ -0,0 +1,115 @@
+package cn.hutool.core.thread;
+
+import cn.hutool.core.thread.RecyclableBatchThreadPoolExecutor.Warp;
+import org.junit.jupiter.api.Test;
+
+import java.util.*;
+import java.util.concurrent.ExecutionException;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.function.Function;
+
+/**
+ * {@link RecyclableBatchThreadPoolExecutor} 测试类
+ */
+public class RecyclableBatchThreadPoolExecutorTest {
+
+
+	/**
+	 * 批量处理数据
+	 * @throws InterruptedException
+	 */
+	@Test
+	public void test() throws InterruptedException {
+		int corePoolSize = 10;// 线程池大小
+		int batchSize = 100;// 每批次数据量
+		int clientCount = 30;// 调用者数量
+		test(corePoolSize,batchSize,clientCount);
+	}
+
+	/**
+	 * 普通查询接口加速
+	 */
+	@Test
+	public void test2() {
+		RecyclableBatchThreadPoolExecutor executor = new RecyclableBatchThreadPoolExecutor(10);
+		long s = System.nanoTime();
+		Warp<String> warp1 = Warp.of(this::select1);
+		Warp<List<String>> warp2 = Warp.of(this::select2);
+		executor.processByWarp(warp1, warp2);
+		Map<String, Object> map = new HashMap<>();
+		map.put("key1",warp1.get());
+		map.put("key2",warp2.get());
+		long d = System.nanoTime() - s;
+		System.out.printf("总耗时：%.2f秒%n",d/1e9);
+		System.out.println(map);
+	}
+
+	public void test(int corePoolSize,int batchSize,int clientCount ) throws InterruptedException{
+		RecyclableBatchThreadPoolExecutor processor = new RecyclableBatchThreadPoolExecutor(corePoolSize);
+		// 模拟多个调用者线程提交任务
+		ExecutorService testExecutor = Executors.newFixedThreadPool(clientCount);
+		Map<Integer, List<Integer>> map = new HashMap<>();
+		for(int i = 0; i < clientCount; i++){
+			map.put(i,testDate(1000));
+		}
+		long s = System.nanoTime();
+		List<Future<?>> futures = new ArrayList<>();
+		for (int j = 0; j < clientCount; j++) {
+			final int clientId = j;
+			Future<?> submit = testExecutor.submit(() -> {
+				Function<Integer, String> function = p -> {
+					try {
+						Thread.sleep(10);
+					} catch (InterruptedException e) {
+						throw new RuntimeException(e);
+					}
+					return Thread.currentThread().getName() + "#" + p;
+				};
+				long start = System.nanoTime();
+				List<String> process = processor.process(map.get(clientId), batchSize, function);
+				long duration = System.nanoTime() - start;
+				System.out.printf("【clientId：%s】处理结果：%s\n处理耗时：%.2f秒%n", clientId, process, duration / 1e9);
+			});
+			futures.add(submit);
+		}
+		futures.forEach(p-> {
+			try {
+				p.get();
+			} catch (InterruptedException | ExecutionException e) {
+				throw new RuntimeException(e);
+			}
+		});
+		long d = System.nanoTime() - s;
+		System.out.printf("总耗时：%.2f秒%n",d/1e9);
+		testExecutor.shutdown();
+		processor.shutdown();
+	}
+	public static List<Integer> testDate(int count){
+		List<Integer> list = new ArrayList<>();
+		for(int i = 1;i<=count;i++){
+			list.add(i);
+		}
+		return list;
+	}
+
+	private String select1()  {
+		try {
+			Thread.sleep(3000);
+		} catch (InterruptedException e) {
+			throw new RuntimeException(e);
+		}
+		return "1";
+	}
+
+	private List<String> select2() {
+		try {
+			Thread.sleep(5000);
+		} catch (InterruptedException e) {
+			throw new RuntimeException(e);
+		}
+		return Arrays.asList("1","2","3");
+	}
+
+}