diff --git a/tools/pnnx/src/CMakeLists.txt b/tools/pnnx/src/CMakeLists.txt
index 113b7a962ec7..c15ba0973d67 100644
--- a/tools/pnnx/src/CMakeLists.txt
+++ b/tools/pnnx/src/CMakeLists.txt
@@ -154,6 +154,7 @@ set(pnnx_pass_level2_SRCS
     pass_level2/F_mish.cpp
     pass_level2/F_normalize.cpp
     pass_level2/F_pad.cpp
+    pass_level2/F_pairwise_distance.cpp
     pass_level2/F_pixel_shuffle.cpp
     pass_level2/F_pixel_unshuffle.cpp
     pass_level2/F_prelu.cpp
diff --git a/tools/pnnx/src/pass_level2/F_pairwise_distance.cpp b/tools/pnnx/src/pass_level2/F_pairwise_distance.cpp
new file mode 100644
index 000000000000..8177b25d52f1
--- /dev/null
+++ b/tools/pnnx/src/pass_level2/F_pairwise_distance.cpp
@@ -0,0 +1,44 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2023 THL A29 Limited, a Tencent company. All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// Unless required by applicable law or agreed to in writing, software distributed
+// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+// CONDITIONS OF ANY KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations under the License.
+
+#include "pass_level2.h"
+
+namespace pnnx {
+
+class F_pairwise_distance : public GraphRewriterPass
+{
+public:
+    const char* match_pattern_graph() const
+    {
+        return R"PNNXIR(7767517
+7 6
+pnnx.Input              input_0         0 1 x1
+pnnx.Input              input_1         0 1 x2
+prim::Constant          op_0     	0 1 p value=%p
+prim::Constant          op_1    	0 1 eps value=%eps
+prim::Constant          op_2    	0 1 keepdim value=%keepdim
+aten::pairwise_distance op_3     	5 1 x1 x2 p eps keepdim out
+pnnx.Output             output          1 0 out
+)PNNXIR";
+    }
+
+    const char* type_str() const
+    {
+        return "F.pairwise_distance";
+    }
+};
+
+REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_pairwise_distance, 10)
+
+} // namespace pnnx
diff --git a/tools/pnnx/tests/CMakeLists.txt b/tools/pnnx/tests/CMakeLists.txt
index e8cb3972b0c7..fab12342b58f 100644
--- a/tools/pnnx/tests/CMakeLists.txt
+++ b/tools/pnnx/tests/CMakeLists.txt
@@ -54,6 +54,7 @@ pnnx_add_test(F_max_pool2d)
 pnnx_add_test(F_max_pool3d)
 pnnx_add_test(F_normalize)
 pnnx_add_test(F_pad)
+pnnx_add_test(F_pairwise_distance)
 pnnx_add_test(F_pixel_shuffle)
 pnnx_add_test(F_pixel_unshuffle)
 pnnx_add_test(F_prelu)
diff --git a/tools/pnnx/tests/test_F_pairwise_distance.py b/tools/pnnx/tests/test_F_pairwise_distance.py
new file mode 100644
index 000000000000..243f61e1b0e0
--- /dev/null
+++ b/tools/pnnx/tests/test_F_pairwise_distance.py
@@ -0,0 +1,58 @@
+# Tencent is pleased to support the open source community by making ncnn available.
+#
+# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# Unless required by applicable law or agreed to in writing, software distributed
+# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+# CONDITIONS OF ANY KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations under the License.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+    def forward(self, x, y):
+        z1 = F.pairwise_distance(x,y,p=1,keepdim=False)
+        z2 = F.pairwise_distance(x,y,p=2,keepdim=True)
+        z3 = F.pairwise_distance(x,y)
+        z4 = F.pairwise_distance(x,y,eps = 1e-3)
+        return z1,z2,z3,z4
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(12, 128, 128)
+    y = torch.rand(12, 128, 128)
+
+    a0,a1,a2,a3 = net(x, y)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x, y))
+    mod.save("test_F_pairwise_distance.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../src/pnnx test_F_pairwise_distance.pt inputshape=[12,128,128],[12,128,128]")
+
+    # pnnx inference
+    import test_F_pairwise_distance_pnnx
+    b0,b1,b2,b3 = test_F_pairwise_distance_pnnx.test_inference()
+
+    return torch.equal(a0,b0) and torch.equal(a1,b1) and torch.equal(a2,b2) and torch.equal(a3,b3)
+
+if __name__ == "__main__":
+    if test():
+        exit(0)
+    else:
+        exit(1)