In order to solve the problem of indoor crowd target scale and attitude diversity and confusion of head targets with surrounding objects, a new Network based on Multi-level Features and hybrid Attention mechanism for indoor crowd detection (MFANet) was proposed. It is composed of three parts:feature fusion module, multi-scale dilated convolution pyramid feature decomposition module, and hybrid attention module. Firstly, by combining the information of shallow features and intermediate layer features, a fusion feature containing context information was formed to solve the problem of the lack of semantic information and the weakness of classification ability of the small targets in the shallow feature map. Then, with the characteristics of increasing the receptive field without increasing the parameters, the dilated convolution was used to perform the multi-scale decomposition on the fusion features to form a new small target detection branch, realizing the positioning and detection of the multi-scale targets by the network. Finally, the local fusion attention module was used to integrate the global pixel correlation space attention and channel attention to enhance the features with large contribution on the key information in order to improve the ability of distinguishing target from background. The experimental results show that the proposed method achieves an accuracy of 0.94, a recall rate of 0.91 and an F1 score of 0.92 on the indoor monitoring scene dataset SCUT-HEAD. All of these three are significantly better than those of other algorithms currently used for indoor crowd detection.