In continuous integration (CI) environments, the program is rapidly and frequently modified and integrated. This feature introduces significant challenges to testing processes conducted in these environments. Based on existing technology, a test case that fails frequently is likely to fail in future tests. Therefore, the historical execution results of test cases are essential to guide the test case prioritization (TCP) in the CI environment. Reinforcement learning involves solving sequential decision-making problems and is suitable for TCP in the CI environment. At present, most of the TCP techniques based on reinforcement learning rely on the current cycle historical failure information of test cases. They rarely consider more historical cycle information, as well as other influencing factors. In this paper, we discussed the occurrence frequency of test cases for the first time. We also considered all historical information of each test case and proposed three new reward function, which employs the percentage of historical failure and the failure distribution of test cases, which can guide the reinforcement learning process. We evaluate our method on five industrial data sets. The experimental results show that our method can effectively prioritize test cases and improve the cost-effectiveness of the CI process.