Computer Evaluation Method and Algorithm of Railway Transportation Service Quality

With the development of science and technology and the improvement of people's requirements for service quality, how to evaluate the service quality of railway transportation scientifically and objectively has become an important issue. In this study, a set of evaluation model frameworks based on computer technology is developed. A comprehensive evaluation of railway transportation service quality is realized by integrating diversified data sources, including passenger feedback, operation data, and third-party data. In the data collection and pretreatment stage, the original data are cleaned, duplicated, and standardized to ensure the data quality. Then, in the feature extraction stage, key features related to service quality are extracted, and an evaluation model is constructed using a random forest (RF) algorithm. The experimental results show that the model performs well in accuracy, recall, and F1 score, improving the accuracy and stability of railway transportation service quality evaluation. Compared with other commonly used machine learning algorithms, RF has more advantages in dealing with multi-source data and complex features. This study not only provides powerful decision support for railway operation departments, but also promotes the continuous improvement of railway transportation service quality.