Exploring the local characteristics and value of rural landscapes in the new era, as well as promoting the deep integration of urban and rural development, are crucial for enhancing the connotation construction and quality improvement of Shanghai’s Park City. Significant potential remains for examining regional features and evaluating the influencing factors of rural landscape elements through multi-source data fusion. Using Jiading District of Shanghai as a case study, this research analyzed street view image data and employed multi-class superposition with semantic segmentation methods to extract the local characteristics of various rural landscape elements. Additionally, geographic detectors and the random forest algorithm were utilized to assess the relationship between different driving factors and rural landscape characteristics. The results indicate that: (1) Environmental green view, sky view, road width, building view, facilities diversity, and enclosure integrity in Jiading’s rural landscapes exhibit notable imbalanced distribution characteristics; (2) The blue space, green space, and gray space in Jiading’s rural landscapes display an interwoven distribution pattern; (3) Multi-dimensional factors collectively influence the formation of rural landscape features, with the strongest interaction occurring within the rural industry dimension, particularly between GDP and commercial vitality. Based on these findings, this paper proposes strategies for enhancing the local characteristics of rural landscapes in Jiading District, providing innovative approaches and methods for evaluating and optimizing rural landscape features within the context of Park City development, and offering references for improving rural landscape quality and promoting Park City construction.