Crowd density estimation has important application value in the field of intelligent security prevention. A crowd density estimation method with multi-feature information fusion is proposed to address the problems of large difference in viewpoint change of two-dimensional images, loss of feature spatial information, and difficulties in scale feature and crowd feature extraction. The proposed method encodes the multi-view information of images through the attention mechanism-guided perspective of spatial attention (PSA) method to obtain the spatial global contextual information of the feature map and weaken the influence of viewpoint change. Through the multi-scale information aggregation (MSIA) method, the multi-scale asymmetric convolution and the null convolution with different expansion rates are effectively integrated to obtain more comprehensive image scale and feature information. Finally, the spatial information of the high-level feature map and the semantic information of the low-level feature map are complemented by the detailed semantic feature embedding fusion, and the contextual information and scale information complement each other to improve the accuracy and robustness of the model. The experimental validation is carried out using the ShanghaiTech, Mall, and Worldexpo’10 datasets, and the experimental results show that the performance of the proposed method has been improved compared with those of other comparative methods.