Self-supervised learning (SSL) can extract self-supervised signals from raw data, which holds great potential for improving recommendation performance. However, two key challenges remain in current self-supervised learning-based recommendation methods. First, most self-supervised recommendation models apply random perturbations to the same node and use the generated different results as self-supervised signals. However, due to the extensive homogeneity in recommendation systems, this method ignores the information from neighboring nodes, which affects the recommendation performance. Secondly, while historical interaction information between users and items as well as the social relationship information between users are the focus of current self-supervised learning-based recommendation models, the internal relationships between items are often neglected. This also leads to insufficient self-supervised signals. Based on these challenges, a self-supervised recommendation method based on multiple views is proposed. This method considers perspectives from preference, user, and item, and employs a multi-view joint training approach for self-supervised learning. By combining the social relationships between users, the category relationships between items, and the historical interaction information between users and items, self-supervised signals are fully extracted. Experiments conducted on three real public datasets validate that the proposed multi-view-based self-supervised learning method is effective in improving recommendation performance.