Recent advances of AI applications in various of industries have led to remarkable performance and efficiency. Driven by the great success of datasets and experience sharing, people are exploring more precious datasets with diverse features and longer time range. The promising reasoning information of well-curated student grade datasets is expected to assist young students to find the best of themselves and then improve their learning outcome and study experience. Through data and experience sharing, young students can have a better understanding of their learning condition and possible learning outcomes. Existing course selection systems in Taiwan which offer limited basic enrolling functions fail to provide performance prediction and course arrangement guidance based on their own learning condition. Students now selecting courses with unawareness of their expecting performance. A personalized guide for students on course selection is crucial for how they structure professional knowledge and arrange study schedule. In this paper, we first analyzed what factors can be used on defining learning curve, and discovered the difference between students with different properties and background. Second, we developed a recommendation system based on great amount of grade datasets of past students, and the system can give students suggestions on how to assign their credits based on their own learning curve and students that had similar learning curve. The result of our research demonstrates the feasibility of a new approach on applying big data and AI technology on learning analysis and course selection.