An image recognition method, performed by an electronic device, the method comprising: extracting a plurality of input matrices from a target image through input channels of a convolutional layer in a convolutional neural network model, the convolutional neural network model being executed in the electronic device; obtaining first result matrices by traversing the plurality of input matrices and performing input transformation on the traversed plurality of input matrices through row transformation reconstruction; obtaining second result matrices by performing convolution kernel transformation on the traversed plurality of input matrices through the row transformation reconstruction, comprising: obtaining the second result matrices by performing the convolution kernel transformation on convolution kernel matrices corresponding to the traversed plurality of input matrices; and the convolution kernel matrices are generated during model training of the convolutional neural network model, the convolution kernel matrices being trained by using training samples for the input matrices; performing matrix multiplication reconstruction on the first result matrices and the second result matrices, to obtain to-be-transformed matrices; performing output transformation on the to-be-transformed matrices through the row transformation reconstruction, to obtain a plurality of output matrices; based on traversing all of the plurality of input matrices, splicing the plurality of output matrices and outputting the spliced plurality of output matrices through output channels of the convolutional layer to obtain a convolution result, each spliced output matrix among the plurality of spliced output matrices corresponding to each input matrix among the plurality of input matrices; and obtaining an image recognition result by inputting the convolution result into a pooling layer in the convolutional neural network model to perform an image recognition on the target image. extracting a plurality of input matrices from a target image through input channels of a convolutional layer in a convolutional neural network model, the convolutional neural network model being executed in the electronic device; obtaining first result matrices by traversing the plurality of input matrices and performing input transformation on the traversed plurality of input matrices through row transformation reconstruction; obtaining second result matrices by performing convolution kernel transformation on the traversed plurality of input matrices through the row transformation reconstruction, comprising: obtaining the second result matrices by performing the convolution kernel transformation on convolution kernel matrices corresponding to the traversed plurality of input matrices; and the convolution kernel matrices are generated during model training of the convolutional neural network model, the convolution kernel matrices being trained by using training samples for the input matrices; performing matrix multiplication reconstruction on the first result matrices and the second result matrices, to obtain to-be-transformed matrices; performing output transformation on the to-be-transformed matrices through the row transformation reconstruction, to obtain a plurality of output matrices; based on traversing all of the plurality of input matrices, splicing the plurality of output matrices and outputting the spliced plurality of output matrices through output channels of the convolutional layer to obtain a convolution result, each spliced output matrix among the plurality of spliced output matrices corresponding to each input matrix among the plurality of input matrices; and obtaining an image recognition result by inputting the convolution result into a pooling layer in the convolutional neural network model to perform an image recognition on the target image. <EOS>