diff --git a/2024.06.03_pytorch_tensor_cat/numpy_concatenate.py b/2024.06.03_pytorch_tensor_cat/numpy_concatenate.py
new file mode 100644
index 0000000..ca00f84
--- /dev/null
+++ b/2024.06.03_pytorch_tensor_cat/numpy_concatenate.py
@@ -0,0 +1,17 @@
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/41194
+"""
+
+import numpy as np
+
+a = np.random.rand(2, 3)
+b = np.random.rand(2, 3)
+
+# 第一维度的数据合并（需要其他的维度保持一致）
+concatenated_1 = np.concatenate((a, b), axis=0)
+print(concatenated_1.shape)
+
+# 第二维度的数据合并（需要其他的维度保持一致）
+concatenated_2 = np.concatenate((a, b), axis=1)
+print(concatenated_2.shape)
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diff --git a/2024.06.03_pytorch_tensor_cat/pytorch_cat.py b/2024.06.03_pytorch_tensor_cat/pytorch_cat.py
new file mode 100644
index 0000000..59d803b
--- /dev/null
+++ b/2024.06.03_pytorch_tensor_cat/pytorch_cat.py
@@ -0,0 +1,40 @@
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/41194
+"""
+
+import torch
+
+# 定义两个张量
+tensor1 = torch.randn(2, 3)
+tensor2 = torch.randn(2, 3)
+print(tensor1)
+print(tensor2)
+print()
+
+# 第一维度的数据合并（需要其他的维度保持一致）
+result1 = torch.cat((tensor1, tensor2), dim=0)
+print(result1)
+print(result1.shape)
+print()
+
+# 第二维度的数据合并（需要其他的维度保持一致）
+result2 = torch.cat((tensor1, tensor2), dim=1)
+print(result2)
+print(result2.shape)
+print()
+
+
+
+# 定义多个张量
+tensor1 = torch.randn(2, 10)
+tensor2 = torch.randn(2, 20)
+tensor3 = torch.randn(2, 30)
+tensor4 = torch.randn(2, 50)
+
+# 将这些张量放在一个列表中
+tensors = [tensor1, tensor2, tensor3, tensor4]
+
+# 第二维度的数据合并（确保所有张量的第一维度相同）
+result3 = torch.cat(tensors, dim=1)
+print(result3.shape)
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