To address the problems of poor targeted performance， unclear classification and lack of datasets faced by agricultural news， an agricultural news classification model based on Enhanced Representation through kNowledge IntEgration （ERNIE）， Deep Pyramidal Convolutional Neural Network （DPCNN） and Bidirectional Gated Recurrent Unit （BiGRU）， called EGC， was proposed. The dataset was first encoded by using ERNIE， then the features of the news text were extracted simultaneously by using the improved DPCNN and BiGRU， and the features extracted were combined and the final results were obtained by Softmax. To make EGC model more suitable for applications in the field of agricultural news classification， the DPCNN was improved by reducing its convolution layers to preserve more features. Experimental results show that compared with ERNIE， the precision， recall and F1 score of the proposed EGC model are improved by 1.47， 1.29 and 1.42 percentage points， respectively， verifying that EGC is better than traditional classification models.