The accuracy of the traditional text classification method is not high enough for naval text classification task.According to the regularity of location distribution of key information in the naval military text, the traditional one-dimensional Convolutional Neural Network (CNN) is improved, and the naval text classification model is designed.In the aspect of one-dimensional convolution, a variable-step convolution method is proposed.The text features are mined by using low step-size at the beginning and end of the text, and high step-size in the middle, so as to improve the mining ability of key features at the beginning and end of the text.In the aspect of one-dimensional pooling, a weighted pooling method is proposed to convert text location information into weighted values to participate in the pooling operation, reflecting the importance of text location information.Experimental result shows that, compared with the traditional support vector machine, K-neighbour algorithm, the traditional one-dimensional CNN and the long/short-term memory network, our text classification model has higher accuracy rate,recall rate and F1 value.