Weather conditions have great influence on the imaging performance of outdoor video equipment. In order to achieve the adaptive adjustment of imaging equipment in inclement weather,so as to improve the effect of intelligent monitoring system,by considering the characteristics that the traditional weather image classification methods have bad classification effect and cannot classify similar weather phenomena,and aiming at the low accuracy of deep learning methods on the weather recognition,a feature fusion model combining traditional methods with deep learning methods was proposed. In the fusion model,four artificially designed algorithms were used to extract traditional features,and AlexNet was used to extract deep features. The eigenvectors after fusion were used to discriminate the image weather conditions. The accuracy of the fusion model on a multi-background dataset reaches 93. 90%,which is better than those of three common methods for comparison,and also performs well on the Average Precision(AP)and Average Recall(AR)indicators;the model has the accuracy on a single background dataset reached 96. 97%,has the AP and AR better than those of other models,and can well recognize weather images with similar features. The experimental results show that the proposed feature fusion model can combine the advantages of traditional methods and deep learning methods to improve the accuracy of existing weather image classification methods,as well as improve the recognition rate under weather phenomena with similar features.