This study focuses on the application and research of XGBoost algorithm and feature engineering in text recognition in the automotive field. First, data sources from multiple parties are obtained to form multi-source heterogeneous data sources. Secondly, feature engineering is constructed, which includes removing noise using regular expressions, text normalization, word segmentation and part-of-speech tagging of sentences, and the construction of text feature space. Then, the principle of the XGBoost algorithm and its advantages in text feature processing are analyzed, and an efficient text recognition model is built. Finally, multiple models are selected for parameters. The results show that the rational application of feature engineering and the XGBoost algorithm can significantly improve the accuracy and stability of text recognition, providing new ideas and methods for the development of text recognition technology. This method provides a new theoretical framework and practical path for the innovation of text recognition technology in the automotive field and contributes to the development research of China's automotive industry.