In oil and gas exploration, evaluation and development, lithology identification and thin sections identification are very important basic tasks, and accurate thin sections identification results can provide a reliable basis for exploration and development. Traditional manual judgment methods or laboratory analysis methods have problems such as strong subjectivity, low efficiency, and low degree of automation. At present, content-based intelligent image recognition technology still faces many difficulties in terms of accuracy and specific applications. Based on the relevant research results at home and abroad and the characteristics and requirements of core sections images in oil and gas exploration and development, the paper successfully designed and developed a thin section image automatic recognition system and a thin section intelligent identification system. Using image gradient distribution and color analysis to intelligently classify igneous rock thin sections, all pixels are classified to obtain the overall identification result, which realizes time-saving, efficient, and high-precision intelligent identification of thin sections.