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In the QA community of Chinese Agricultural Technology Promotion APP, thousands of rice text data questions are added every day, and the rapid and automatic classification of questions is a key step to realize the intelligent QA system of rice. However, due to the high dimensional sparsity of text data and the particularity of agricultural problems, the classification of rice questions faces difficult challenges. In order to improve the classification performance of rice question text, a convolution text classification method with dense connection was proposed. A dense connection between upstream and downstream convolution blocks was established, which enabled the model to synthesize large-scale features from small-scale features. Combined with the agricultural word segmentation dictionary, the text data was segmented into 100-dimensional word vectors by Word2vec. Neural network model’s parameters for question classification in rice question answering system were obtained by training text data with dense concatenated convolution model and attention mechanism. The experimental results showed that the text classification model based on Attention_DenseCNN can optimize the text’s representation and feature extraction, and also it can automatically classify the rice question text with accuracy of 95.6% and F1 value of 94.9%. Compared with the other seven text classification methods, the classification performance had obvious advantages.