Abstract: To prevent the rapid spread of negative information in social networks, it is necessary to identify influential social network nodes by evaluating the influence of users in social networks. In response to the problem of missing cross characteristics in traditional algorithms in the field of social networks, this paper proposes a network user influence evaluation model combined with Gaussian Bayesian-derived classifiers. The model first combines user activity, contact, coverage, and other dimensions to establish social network user influence characterisation indicators. At the same time, it considers the relationship characteristics between social network users and the user's behaviour characteristics to reduce the impact of zombie fans and garbage social networks on network evaluation results. A model-solving method based on a Gaussian Bayesian-derived classifier was proposed by establishing a continuous attribute naive Bayes classifier method. The key factors affecting the evaluation model were analysed in depth using 152059423 media and newspaper user comments from Sina Weibo as experimental data. A comparative experiment with traditional models such as HRank was completed using simulation software to verify the feasibility of the model. The experimental results indicate that the model reflects the cross characteristics of social network users and improves the model's practicality. Compared to traditional algorithms, this model tends to have more stable classification errors, lower error rates in classification results, and better adaptability.