Herein, a classification method based on an improved deep convolutional neural network is proposed to address the problem that the artificial classification of bobbins is time-consuming, labor-intensive, and not sufficiently intelligent in the actual production of textile mills. First, the original network structure was improved based on the AlexNet neural network model framework. All convolutional layers used 3×3 size convolution kernels and multiple convolution kernels in series to extract more abstract features of objects. Next, we reintegrated the sliding average, conduct L2 regularization, and used other tricks to improve the generalization ability. Moreover, the L_ReLU activation function was used to avoid the “death” phenomenon of some neurons. Consequently, the test samples were input into the trained neural network to achieve the classification of bobbins. Experimental results show that the recognition rate of the method is 88.2%, which is approximately 15 percentage points higher than that by the traditional classification method. Compared with other neural network models, the proposed method demonstrates the advantages of high recognition rate and short time, which complies with the actual industrial requirements.