Xiaoqiong Tong,Mengmu Ruan

With industrial upgrading and the deepening development of the digital economy, the financial sector is undergoing a profound transformation from computerization to intelligentization, and from accounting-oriented to decision-support driven. As an efficient, flexible programming language with a strong ecosystem, Python has become a key technical tool in the era of intelligent finance. Against this backdrop, vocational undergraduate education—as the primary platform for cultivating high-level technical talents—must inevitably integrate Python technology into its intelligent finance courses, marking an imperative trend. However, the current teaching practice generally faces practical predicaments such as the disconnection between curriculum content and industry application, weak practical teaching components, and insufficient competencies of the faculty team. This study delves deeply into the main application scenarios of Python technology in intelligent finance, systematically analyzes the key challenges faced by vocational undergraduate education in conducting related teaching, and builds a practice teaching pathway centered on “curriculum content reconstruction, practice system rebuilding, co-construction of faculty team, and reform of assessment methods” from the perspective of industry-education integration. This paper emphasizes that by deepening school-enterprise cooperation and integrating real enterprise projects, data and cases into the whole teaching process, it is possible to effectively bridge the gap between theory and practice, and cultivate interdisciplinary and application-oriented financial talents capable of proficiently utilizing Python technology to solve modern financial problems.

Industry-Education Integration; Python Technology; Vocational Undergraduate Education; Intelligent Finance; Curriculum Practice

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