Aiming at the problems such as the lack of consideration of space and other factors in the traditional key intersection identification model and the lack of objectivity in the weight distribution of intersection evaluation indicators in the urban traffic road network, an improved CRITIC (Criteria Importance Though Intercrieria Correlation)-independent weight method based on fusion is proposed. The improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. Aiming at the defects of the CRITIC method, the Theil index and the grey correlation degree are introduced to improve the CRITIC method; the independent weight method is used to eliminate the collinearity between the evaluation indexes and assign the weights of the evaluation indexes; in order to overcome the defects of using Euclidean distance as the distance measurement in TOPSIS , the weighted Mahalanobis distance is introduced to replace the Euclidean distance, and the key intersection identification is realized according to the closeness. The simulation results show that compared with the traditional key intersection identification model, the model proposed in this study can identify key intersections more accurately and comprehensively, which fully verifies the validity and accuracy of the model proposed in this study.