diff --git a/mobile/src/operators/kernel/central-arm-func/softmax_arm_func.h b/mobile/src/operators/kernel/central-arm-func/softmax_arm_func.h
index a94c8299c514bc9e2937daf57b1a845d7be56b16..29d63937ba59debf75da6ac5c5d31d50ab6abfa7 100644
--- a/mobile/src/operators/kernel/central-arm-func/softmax_arm_func.h
+++ b/mobile/src/operators/kernel/central-arm-func/softmax_arm_func.h
@@ -18,6 +18,44 @@ limitations under the License. */
 #include "operators/op_param.h"
 namespace paddle_mobile {
 namespace operators {
+
+void softmax_basic_axis_float(const float *din, float *dout,
+                              const int axis_size, const int inner_num,
+                              const int outer_num) {
+  int compute_size = inner_num * outer_num;
+#pragma omp parallel for
+  for (int i = 0; i < compute_size; ++i) {
+    int idx_inner = i % inner_num;
+    int idx_outer = (i / inner_num) * axis_size;
+    int real_index = idx_outer * inner_num + idx_inner;
+
+    float max_data = din[real_index];
+    // get max
+    for (int j = 1; j < axis_size; ++j) {
+      real_index += inner_num;
+      max_data = din[real_index] > max_data ? din[real_index] : max_data;
+    }
+
+    real_index = idx_outer * inner_num + idx_inner;
+    // sub, exp and sum
+    dout[real_index] = expf(din[real_index] - max_data);
+    float sum_data = dout[real_index];
+    for (int j = 1; j < axis_size; ++j) {
+      real_index += inner_num;
+      dout[real_index] = expf(din[real_index] - max_data);
+      sum_data += dout[real_index];
+    }
+
+    float sum_inv = 1.f / sum_data;
+    real_index = idx_outer * inner_num + idx_inner;
+    // get softmax result
+    for (int j = 0; j < axis_size; ++j) {
+      dout[real_index] *= sum_inv;
+      real_index += inner_num;
+    }
+  }
+}
+
 template <typename P>
 void SoftmaxCompute(const SoftmaxParam<CPU> &param) {
   const Tensor *in_x = param.InputX();
@@ -25,7 +63,29 @@ void SoftmaxCompute(const SoftmaxParam<CPU> &param) {
   auto x_dims = in_x->dims();
   out->Resize(x_dims);
   out->mutable_data<float>();
-  math::SoftmaxFuntor<CPU, float>()(in_x, out);
+  if (param.has_axis_) {
+    int axis = param.axis_;
+    int axis_size = x_dims[axis];
+    auto x_rank = x_dims.size();
+    DLOG << "x_rank :" << x_rank;
+
+    if (axis < 0) {
+      axis += x_rank;
+    }
+
+    DLOG << "axis :" << axis;
+
+    int outer_num = framework::product(framework::slice_ddim(x_dims, 0, axis));
+    DLOG << "outer_num :" << outer_num;
+    int inner_num =
+        framework::product(framework::slice_ddim(x_dims, axis + 1, x_rank));
+    DLOG << "inner_num :" << inner_num;
+
+    softmax_basic_axis_float(in_x->data<float>(), out->data<float>(), axis_size,
+                             inner_num, outer_num);
+  } else {
+    math::SoftmaxFuntor<CPU, float>()(in_x, out);
+  }
 }
 }  // namespace operators
 }  // namespace paddle_mobile
diff --git a/mobile/src/operators/op_param.h b/mobile/src/operators/op_param.h
index b49f3fce320ce6fe0a350bed2f223c2a74e56047..4cce4f4914a95661485ef320ae013b13548f30e2 100644
--- a/mobile/src/operators/op_param.h
+++ b/mobile/src/operators/op_param.h
@@ -1180,10 +1180,17 @@ class SoftmaxParam : public OpParam {
       : OpParam(inputs, outputs, attrs, scope) {
     input_x_ = InputXFrom<GType>(inputs, *scope);
     out_ = OutFrom<GType>(outputs, *scope);
+    if (HasAttr("axis", attrs)) {
+      axis_ = GetAttr<int>("axis", attrs);
+      has_axis_ = true;
+    }
   }
   const GType *InputX() const { return input_x_; }
   GType *Out() const { return out_; }
 
+  int axis_ = -1;
+  bool has_axis_ = false;
+
  private:
   GType *input_x_;
   GType *out_;