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Aiming at the problems of time-consuming and low accuracy of traditional embroidery classification methods in the inheritance and protection of traditional Chinese embroidery technology, an embroidery image classification method based on improved DenseNet is proposed. The local binary pattern, Canny operator edge extraction and Gabor filtering are used to extract the texture feature and the original image, which are merged into a four to six-channel image data set and sent to the network to expand the data set width. The Spatial Pyramid Pooling (SPP) structural optimization model is proposed to accelerate the convergence rate of loss. The Leaky ReLU activation function is used to optimize the ReLU function to improve the classification and recognition accuracy. The simulation results show that the embroidery image classification and recognition method based on the improved DenseNet can solve the problems existing in the traditional embroidery image classification method. The accuracy of the improved model is 8.1 % higher than that of the benchmark model, as high as 97.39 %.