To improve the accuracy of identification of rice pest，an algorithm was proposed in this study． Firstly，the U-Net network of deep semantic segmentation was used to remove the influence of complex background． Then，the sliding window method was used to extract the HSV color feature and the scale-invariant local ternary pattern (SILTP) texture feature of rice pest images． The feature vector was formed by gathering the maximum value of the features in the same horizontal sliding window． Then the discriminative features of the rice pest images were ob-tained by the Relief-F optimization algorithm． At the same time，an elliptic metric with good distinguishability for data was introduced in this study． The nonlinear transformation that reflects the space structure and semantic infor-mation of rice pest image features was determined via elliptic metric learning． Then the model for the potential rela-tionship among rice pest image features was built． This model reduced the distance between same-class features but increases the distance between different-class features． In elliptic metric learning，the regular term of the Frobenius norm was added to the triple constraint function to avoid overfitting and improve generalization ability． Finally，the elliptic metric matrix was used to transform the features of the rice pest images into a new feature space． Thereby，the identification performance of the SVM classifier was improved． The identification results of 13 kinds of common rice pests show that the proposed method can significantly promote the few-shot identification accuracy of rice pest images in complex background and can provide valuable reference for the intelligent recognition of crop pest in pre-cision agriculture．