The rapid development of social networks has a wide range of social effects, which facilitates the study of social issues. Accurately forecasting the information propagation process within social networks is crucial for promptly understanding the event direction and effectively addressing social problems in a scientific manner. The relationships between non-adjacent users and the attitudes of users significantly influence the information propagation process within social networks. However, existing research has ignored these two elements, which poses challenges for accurately predicting the information propagation process. This limitation significantly hinders the study of emotional contagion and influence maximization in social networks. To address these issues, by considering the relationships between non-adjacent users and the influence of user attitudes, we propose a new information propagation model based on the independent cascade model. Experimental results obtained from six real Weibo datasets validate the effectiveness of the proposed model, which is reflected in increased prediction accuracy and reduced time complexity. Furthermore, the information dissemination trend in social networks predicted by the proposed model closely resembles the actual information propagation process, which demonstrates the superiority of the proposed model.