Well logging identification and quantitative characterization of source rocks are of great significance to hydrocarbon reserves evaluation. There are three types of source rocks in the Jurassic Yangxia Formation of Kuqa Depression: coal measure, dark mudstone and carboniferous mudstone. However, due to the depth of burial (average greater than 4500 m), the source rocks are too mature and affected by deep buried ground stress, the traditional ΔlogR method is difficult to apply. Based on this comprehensive use of geochemical analysis and geophysical logging data, this paper first reveals the geological characteristics of the source rocks of the Jurassic Yangxia Formation, and realizes the qualitative identification of the source rocks of different lithologies through conventional logging crossplot. The results show that the overall maturity of source rocks of Yangxia Formation is high and the quality of source rocks is medium to good. The qualitative identification of source rocks can be realized by the intersection of curves such as GR, AC, DEN, CNC and AC, GR, RT, CNC, DEN curves that are sensitive to the response of the source rock are selected to establish a quantitative prediction model of TOC content by using multi-regression analysis methods and BP artificial intelligence method, and the quantitative evaluation of the single well source rock in the study area is realized. The results are in good agreement with the measured core data. The research results are of guiding significance for comprehensive logging evaluation of deep source rock quality in Kuqa Depression.