Aiming at the problems of the attribute items of customer credit data, such as many dimensions, large number and complexity, this paper proposes a multi-perspective decision-making fusion personal credit evaluation method based on similarity measurement. The innovation of this method lies in that it can carefully consider the geometric shapes of different credit data, divide the data from multiple angles, and carry out similarity matching. In addition, it makes full use of the self-consistency that random forest can carry out feature extraction to improve the accuracy and robustness synchronization of the model. The experimental results on UCI dataset show that the performance of the three distance measures is greatly improved after feature extraction and outlier removal, and the fluctuation range of the recognition rate is significantly reduced compared with that before data preprocessing, which shows that the optimized model has stronger robustness. By combining the three measures, credit information can be integrated from multiple angles, which makes the identification performance significantly better than that of a single measure, and the performance is better compared with other classical combination methods. The combination of random forest and distance measure in the field of personal credit evaluation adds new experience to the diversity of personal credit evaluation methods.