With the rapid development of Internet technology, online course learning platforms like MOOCs have gained wide popularity in recent years. In order to facilitate the personalized smart education of "teach students in accordance with their aptitude", artificial intelligence technologies, represented by recommendation algorithms, have been widely focused by communities of academia and industry. Although it has been successfully applied in e-commerce and other fields, the integration of recommendation algorithms into online education scenarios still faces severe challenges: Existing algorithms are not competent in mining implicit interactives, guidance from knowledge towards recommendation is not effective, and there are still few sights on practical recommendation system software. Therefore, an intelligent course recommendation system is proposed for industrial scenarios, which includes: (1) an offline recommendation engine based on graph convolutional neural network, modeling the "user-course" implicit interaction behavior as a heterogeneous graph, and extracting course knowledge information to guide the model learning and training. Therefore, the relationship like "user-course-knowledge" can be fully and deeply mined; (2) an efficient online recommendation system prototype based on multi- stage pipeline of "preprocess-recall-offline sort-online recommend-result fuse". Besides quickly responding to course recommendation requests, it can effectively alleviate the major obstacle to recommender systems, the cold start problem. Finally, based on a course learning dataset from real platform, extensive experiments are conducted to show that the proposed offline recommendation engine is competitive with other mainstream recommendation algorithms. And based on the analysis of two typical use cases, the usability of the proposed online recommendation system when facing industrial needs is verified.