Most existing crowd counting methods use convolution operations to extract features. However, extracting and transmitting spatial diversity feature information are difficult. In this paper, we propose an Involution-improved single-column deep crowd-counting network to mitigate these problems. Using VGG-16 as the backbone, the proposed network uses an Involution operator combined with residual connection to replace the convolution operation, thereby enhancing the perception and transmission for spatial feature information. The dilated convolution was adopted to expand the receptive field while maintaining resolution to enrich deep semantic features. Additionally, we used the joint loss function to supervise the network training, improving counting accuracy and global information correlation. Compared with the baseline model, the performance of the proposed method across the ShangHaiTech, UCF-QNRF, and UCF_CC_50 datasets considerably is improved, demonstrating that our approach outperforms many current advanced algorithms. Furthermore, results show that the proposed crowd counting method has higher accuracy and better robustness than other methods.