In recent years, with the continuous improvement of environmental governance requirements, automobile exhaust becomes one of the main sources of pollution. The automobile industry is the focus of attention in China nowadays, and the driving conditions of automobiles are considered as reflections in the automobile industry. Therefore, the study of working conditions has become one of the urgently needed research projects. Based on various industry parameters of automobile driving conditions, this study develops a general method for reflecting automobile driving conditions in different regions of China. At the same time, we use the dimensionality reduction method of the de-noise encoder used in deep learning when reducing dimensionality of complex data, and has achieved good and practical experimental results. The data in this paper is derived from the automobile experiment in Jiading District, Shanghai. After processing the data, the construction of the general working condition map has been realized through different means and methods such as EMD, feature extraction, dynamic time planning, wavelet decomposition, etc., providing a reference for the construction of the general working conditions map of the city and the overall.