In the field of fine-grained visual recognition， due to subtle differences between highly similar categories， precise extraction of subtle image features has a crucial impact on recognition accuracy. It has become a trend for the existing related hot research algorithms to use attention mechanism to extract categorical features， however， these algorithms ignore the subtle but distinguishable features， and isolate the feature relationships between different discriminative regions of objects. Aiming at these problems， a fine-grained image recognition algorithm based on mid-level subtle feature extraction and multi-scale feature fusion was proposed. First， the salient features of image were extracted by using the weight variance measures of channel and position information fused mid-level features. Then， the mask matrix was obtained through the channel average pooling to suppress salient features and enhance the extraction of subtle features in other discriminative regions. Finally， channel weight information and pixel complementary information were used to obtain multi-scale fusion features of channels and pixels to enhance the diversity and richness of different discriminative regional features. Experimental results show that the proposed algorithm achieves 89.52% Top-1 accuracy and 98.46% Top-5 accuracy on dataset CUB-200-211， and 94.64% Top-1 accuracy and 98.62% Top-5 accuracy on dataset Stanford Cars， and 93.20% Top-1 accuracy and 97.98% Top-5 accuracy on dataset Fine-Grained Visual Classification of Aircraft （FGVC-Aircraft）. Compared with recurrent collaborative attention feature learning network PCA-Net （Progressive Co-Attention Network） algorithm， the proposed algorithm has the Top-1 accuracy increased by 1.22， 0.34 and 0.80 percentage points respectively， and the Top-5 accuracy increased by 1.03， 0.88 and 1.12 percentage points respectively.