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
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+++ b/hutool-core/src/main/java/cn/hutool/core/thread/RecyclableBatchThreadPoolExecutor.java
@@ -0,0 +1,210 @@
+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.stream.Collectors;
+import java.util.stream.Stream;
+
+/**
+ * 可召回批处理线程池执行器
+ * 1.数据分批并行处理
+ * 2.线程安全，可用同时执行多个任务
+ * 3.主线程、线程池混合执行，主线程空闲时会尝试召回线程池队列中的任务执行，无需担心任务阻塞
+ *
+ * @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
+	 * 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
+	 * 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
+	 *
+	 * @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();
+	}
+
+	/**
+	 * 分批次处理数据
+	 * 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.异常在{@link Function}中自行处理
+	 * 3.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
+	 *
+	 * @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
+	 */
+	@SuppressWarnings("unchecked")
+	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();
+		ConcurrentLinkedQueue<IdempotentTask<R>> taskQueue = new ConcurrentLinkedQueue<>();
+		Map<Integer,Future<ResultWarp<R>>> futuresMap = new HashMap<>();
+		// 提交前 batchCount-1 批任务
+		for (int i = 0 ; i < batchCount-1 ; 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));
+		}
+		Object[] arr = new Object[batchCount];
+		// 处理最后一批
+		arr[batchCount-1] = processBatch(batches.get(batchCount-1), processor);
+		// 处理剩余任务
+		processRemainingTasks(taskQueue, futuresMap,arr);
+		//排序、过滤null
+		return Stream.of(arr)
+				.filter(Objects::nonNull)
+				.map(p -> (List<R>) p)
+				.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 arr 结果存储数组
+	 */
+	private <R> void processRemainingTasks(Queue<IdempotentTask<R>> taskQueue, Map<Integer,Future<ResultWarp<R>>> futuresMap,Object[] arr) {
+		// 主消费未执行任务
+		IdempotentTask<R> task;
+		while ((task = taskQueue.poll()) != null) {
+			try {
+				ResultWarp<R> call = task.call();
+				if (call.effective) {
+					// 取消被主线程执行任务
+					Future<ResultWarp<R>> future = futuresMap.remove(task.index);
+					future.cancel(false);
+					//加入结果集
+					arr[task.index] = call.result;
+				}
+			} catch (Exception e) {
+				// 不处理异常
+				throw new RuntimeException(e);
+			}
+		}
+		futuresMap.forEach((index,future)->{
+			try {
+				ResultWarp<R> resultWarp = future.get();
+				if(resultWarp.effective){
+					arr[index] = resultWarp.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<ResultWarp<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 ResultWarp<R> call() throws Exception {
+			if (executed.compareAndSet(false, true)) {
+				return new ResultWarp<>(delegate.call(), true);
+			}
+			return new ResultWarp<>(null, false);
+		}
+	}
+
+	/**
+	 * 结果包装类，标记结果有效性
+	 */
+	private static class ResultWarp<R>{
+		private final List<R> result;
+		private final boolean effective;
+		ResultWarp(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
+	 */
+	public 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
+	 */
+	public static <T,R> List<R> processBatch(List<T> batch, Function<T,R> processor) {
+		return batch.stream().map(processor).filter(Objects::nonNull).collect(Collectors.toList());
+	}
+
+}