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With the rapid development of mobile internet, short text data of APPs has exploded. In the field of agriculture, tens of thousands of questions about agricultural technology have been put forward in agro-technical extension community. Accurate classification is the basis of agricultural intelligent Q&A and the guarantee of precise information service. In order to improve the performance of data classification,，a short text classification method based on BiGRU_MulCNN model was proposed to overcome the limitations of the classification process, such as few vocabulary, sparse features, large amount of data, lots of noise and poor normalization. In the model, Jieba word segmentation tools and agricultural dictionary were selected to text segmentation, then TF-IDF algorithm was adopted to expand the text characteristic and weighted word vector according to the text of key vector, and bi-directional gated recurrent unit was applied to catch the context feature information, multi-convolutional neural networks was finally established to gain local multidimensional characteristics of text. Batch-normalization, Dropout, Global Average Pooling and Global Max Pooling were involved to solve over-fitting problem. The results showed that the model could classify questions accurately, with an accuracy of 95.9%. Compared with other models, such as CNN model, RNN model and CNN/RNN combinatorial model, BiGRU_MulCNN had obvious advantages in classification performance in intelligent agro-technical information service.