This study is dedicated to exploring the complex process of opinion formation in social networks, with a particular focus on the mechanisms of consensus achievement in decentralized environments. A novel opinion classification strategy, termed “the second confidence interval” is proposed to improve the traditional DeGroot consensus model, and two distinct opinion dynamics models are developed: the far attack inbreeding (FAI) model and the outbred recent attack (ORA) model. These models comprehensively consider the degree of individual acceptance and emphasis on surrounding opinions. In addition, through an in-depth analysis of neighborhood opinions in social networks, a comprehensive setup of the individual model is carried out, covering multiple factors such as private opinions, expressed opinions, obstinacy, and preferences. The results indicate that under specific parameter settings, both the FAI and ORA models can reach a consensus more rapidly than the original DeGroot model. Specifically, the ORA model converges at around 700 steps, while the convergence speed of the FAI model gradually approaches that of the ORA model with increasing parameter values. Compared with the baseline model, the ORA model exhibits smaller variations in converged opinion values, no more than 3.5%, whereas the FAI model demonstrates greater volatility. These findings not only deepen people’s understanding of the public opinion formation mechanisms in social networks but also highlight the significance of opinion dynamics within individual neighborhoods in the consensus formation process, offering new perspectives and research directions for future studies in this field.