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.

Focused on the issue that the traditional fingerprint matching methods based on minutiae are mainly applicable for large-size fingerprint and the accuracy rate would reduce significantly when dealing with small-size fingerprint from smart phone, a small-size fingerprint matching method based on deep learning was proposed. Firstly, the detailed information of minutiae was extracted from fingerprint images. Secondly, the Regions Of Interest (ROI) were searched and labeled based on minutiae. Then a lightweight deep neural network was built and improved from original residual module. In addition, binary feature pattern and triplet loss were used to optimize and train the proposed model respectively. Finally, the small-size fingerprint matching was accomplished with the fusion strategy of registration and matching. The experimental results show that the Equal Error Rate (EER) of the proposed method can reach 0.50% and 0.58% on public FVC_DB1 and in-house database respectively, which is much lower than the traditional fingerprint matching methods based on minutiae, and can improve the performance of small-size fingerprint matching effectively and meet the requirements on smart phone.

There are various abnormal phenomena before the earthquake, and how to effectively extract exception information before the earthquake is a very important research topic. The geometric moving average martingale algorithm based on sliding window was proposed to extract the anomaly features before earthquake. The seismic data were processed by geometric moving average martingale and sliding window feature extraction, and the anomaly features of earthquake could be effectively extracted before earthquake. Through the analysis of the NOAA (National Oceanic and Atmospheric Administration satellite outgoing long wave radiation information before the Wenchuan earthquake and Lushan earthquake, the experiments show that the algorithm can detect that the earthquake area is more obviously abnormal than the surrounding area. This anomaly can help researchers determine earthquake area before the earthquake.