and low reliability seriously limit the long-term development of public transport systems. At the same time, the improvement of the public transportation network is accompanied by increasingly close connections with other social systems, and the complexity of various types of data has increased significantly beyond the processing capacity of traditional data analysis tools. Therefore, the use of machine learning methods based on integrated analysis of multi-source data to definite, evaluate, and cause analysis of the factors affecting the health of urban bus operation have become urgent research. At present, domestic and international research on public transport operation diagnosis mainly focuses on the construction of diagnostic index systems and the selection of diagnostic methods. [PERSON] chose measure departure time, running time, and arrival interval reliability as evaluation indicators. Afterwards, a comprehensive evaluation system for bus service reliability was constructed and applied to the actual evaluation in the context of the routes [3]. [PERSON] integrated four indicators, including measures of journey time consistency, into a valid composite indicator set. The panel data analysis was used to provide confidence intervals for the composite indicator values for each direction of travel for each bus route to quickly determine the operating conditions of the route [4]. [PERSON] established a public transport service evaluation index system with convenience, reliability, and comfort as the objectives [5]. [PERSON] established an intercity bus travel satisfaction evaluation system based on improved hierarchical analysis and fuzzy comprehensive evaluation methods to examine the economy, reliability, convenience, comfort, and accessibility of the bus system, respectively [6]. The usual approach to the study of health diagnostic methods is to calculate weights based on a system of indicators and then use the results of the calculations to measure them. The weight determination methods include fuzzy comprehensive evaluation method [7; 8], hierarchical analysis method and expert decision method [9; 10], DEA method [11], and entropy value method [12]. Among them, the fuzzy comprehensive evaluation method is a good method for solving fuzzy, difficult to quantify non-deterministic problems and is more adaptable for solving practical problems. However, the selection of its parameters such as the determination of fuzzy matrix and the selection of affiliation degree is difficult and too subjective. Hierarchical analysis and expert decision method are also more subjective evaluation methods, which depend on the evaluator's knowledge of the problem and are not suitable for use alone. DEA (Data Envelopment Analysis) is a quantitative analysis method for evaluating the relative effectiveness of comparable units of the same type based on multiple input indicators and multiple output indicators. The DEA method is actually a linear programming model to determine whether the same type of decision units with similar roles are on the production frontier. The entropy method is an objective evaluation method that can make full use of the available data and determine the index weights according to the volatility of the data [13]. [PERSON] used fuzzy set theory as the basis by investigating passengers' perceptions. A fuzzy weighted average of all indicators was then adopted as a way to evaluate the state of public transport [14]. [PERSON] proposed a GIS-based fuzzy clustering analysis method for the service level of public transport systems. Combining qualitative and quantitative evaluations, the system analysis of public transport service level was completed [15]. Using a three-parameter model and an operational state space model, [PERSON] completed the evaluation of the level of bus service by region and state. The quantitative criteria established by this method are based on a three-dimensional state space and are more objective [16].