With the continuous development of mobile and semantic IoT (Internet of Things), the number of news images is increasing sharply. Automatic semantic annotation technology is an important tool to manage and retrieve news images. Therefore, the study is focused on the automatic semantic annotation of news images. First, the problems of the existing automatic annotation methods of news images are analyzed and studied, and then an automatic semantic annotation method of news images based on the fusion of weight and features is proposed. The proposed method extracts the colour features, texture features, and shape features of news images to determine their stability by using the standard deviation of features, and then calculates the weighting coefficient to realize automatic annotation of the news images. The results of the study in the corel5k dataset show that the accuracy of the weight-feature fusion annotation model is 60% for the colour feature, 62% for the texture feature, and 67% for the mixture of colour, shape, and texture. The accuracy of the fusion of weight and feature annotation method is 7% higher than that of the single feature annotation method. The maximum accuracy of the fusion of weight and feature annotation method is 91.2%. The study provides a reference for the research of automatic annotation of news images.