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Online taxi tourism is one of the important ways of daily tourism. The operator carries out a single evaluation method for the driver's service quality, lacking a comprehensive study of service quality from multiple dimensions of order activity satisfaction, resulting in a high degree of hidden danger to passenger safety and rights. In this paper, an improved principal component analysis (PCA) method, namely Fuzzy C-Mean Clustering (FCM-PCA) based on PCA, is proposed. Experiments show that in the research of target object evaluation, the principal component values and principal component scores of target samples can be used as new indicators for clustering, so as to improve the efficiency of high-dimensional data clustering on the basis of reducing information loss. This study provides a way of thinking for the selection of important service components and a research method for the comprehensive analysis of different drivers' service levels.

Comprehensive Service Level Analysis of Online Taxi Drivers Based on Fuzzy Clustering Combined with Principal Component Analysis