Multi-view clustering aims to learn more comprehensive and accurate consensus representations from the diversity information of different views to improve the clustering performance of the model. Currently, most multi-view clustering algorithms use the Hilbert-Schmidt independence criterion (HSIC) or adaptive weighting method to consider the diversity of each view from a global perspective, ignoring the learning of local diversity information between samples in each view. This study proposes a diversity-guided deep multi-view clustering algorithm to address the above issues. Firstly, the study proposes a soft clustering module integrating multi-head self-attention mechanism. Specifically, the multi-head self-attention mechanism is applied to learn global diversity, and the soft clustering fuzzy C-means algorithm is utilized to learn local diversity. Secondly, a soft clustering module is introduced into the structure of the depth map auto-encoder network to generate potential representations guided by diversity information. Then, the obtained latent representations of each view are weighted and fused to obtain consensus representations, and the spectral clustering algorithm is leveraged to cluster the consensus representations. Finally, comparative experiments and ablation experiments are conducted on three commonly used datasets. The experimental results show that the proposed clustering algorithm has good clustering performance, and the diversity information learning module can effectively improve the clustering performance of the algorithm.