In order to solve the problem of low recognition accuracy, the image captured by mobile robot in the process of target recognition has multi-target interference and the target feature is limited in a single perspective. In this paper, a method of Feature fusion of dynamic visual Angle images based on grouping feature weight weights is proposed. In this method, multiple target features are weighted and grouped by progressive K-means clustering, and continuous image features are fused from dynamic perspective by LSTM network, so as to improve the accuracy of target recognition. The verification results show that the first recognition rate on market-1501 data set reaches 93.80%, and the average accuracy reaches 89.13%, with good experimental results.