创新创业理论研究与实践 ›› 2026, Vol. 9 ›› Issue (4): 101-103.

• 教育改革与发展 • 上一篇    下一篇

油气行业院校人工智能课程建设的思考——以机器学习课程为例

崔立杰1, 刘颖2   

  1. 1.中国石油大学(北京)克拉玛依校区 石油学院,新疆克拉玛依 834000;
    2.中国石油大学(北京)克拉玛依校区 马克思主义学院,新疆克拉玛依 834000
  • 出版日期:2026-02-25 发布日期:2026-07-01
  • 作者简介:崔立杰(1987—),男,内蒙古赤峰人,博士研究生,讲师,研究方向:机器学习辅助地震地质解释应用,电子邮箱:lijiecui2020@outlook.com。
  • 基金资助:
    新疆维吾尔自治区“天池英才”引进计划项目资助“断裂带的地震增强解释以及断裂连接过程对碎屑岩区断裂带结构控制作用研究”; 中国石油大学(北京)克拉玛依校区教育教学研究与改革项目“思维导图在高校计算机类课程教学中的应用:以‘人机交互技术’为例”(JG2024032)

Thoughts on the Construction of Artificial Intelligence Courses in Oil and Gas Industry Colleges—Taking Machine Learning Courses as an Example

CUI Lijie1, LIU Ying2   

  1. 1. College of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay Xinjiang, 834000, China;
    2. College of Marxism, China University of Petroleum-Beijing at Karamay, Karamay Xinjiang, 834000, China
  • Online:2026-02-25 Published:2026-07-01

摘要: 随着人工智能(AI)技术的快速发展,其在各学科领域中发挥着日益重要的作用,尤其在油气行业和地学领域展现了广阔的应用前景。该文以中国石油大学(北京)克拉玛依校区地学专业的机器学习课程为研究对象,探讨了AI交叉学科课程的设计与优化路径。通过系统分析课程教学现状,该文梳理了在地学专业教学中机器学习课程的实践效果与存在的问题,并提出了相应的教学对策与改进建议,以为相关课程的建设提供参考。

关键词: 人工智能, 油气行业, 地学专业, 机器学习, 课程优化, 教学改进

Abstract: With the rapid development of artificial intelligence (AI) technology, its role in various disciplines is becoming increasingly important, especially in the oil and gas industry and geology, showing broad application prospects. This paper takes the machine learning course of geology major in China University of Petroleum-Beijing at Karamay as the research object, and discusses the design and optimization path of AI interdisciplinary courses. By systematically analyzing the current status of course teaching, this article summarizes the practical effects and existing problems of machine learning courses in the teaching of geology majors, and proposes corresponding teaching strategies and improvement suggestions, in order to provide references for the construction of related courses.

Key words: Artificial intelligence, Oil and gas industry, Geoscience major, Machine learning, Course optimization, Teaching improvement

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