In modern FPGA (Field Programmable Gate Array) design, due to the increase in complexity, traditional verification methods are difficult to achieve high test coverage, resulting in limited verification efficiency and quality. This study proposed an FPGA verification method based on test coverage analysis, aiming to improve coverage and verification efficiency. Firstly, this article established a coverage analysis framework by analyzing the existing FPGA verification process and the reasons for insufficient testing coverage. Next, it utilized automated test generation tools to increase the diversity and complexity of testing scenarios, thereby expanding the scope of testing coverage. Then, this article introduced machine learning algorithms based on Long Short-Term Memory (LSTM) networks to adjust test parameters to meet different design requirements. Finally, changes in coverage can be monitored through dynamic tracking and analysis tools, and validation strategies can be adjusted accordingly. The experimental results show that compared to random test generation, manual test case design, and equal probability coverage testing, the method proposed in this study achieved a test coverage rate of 95% in FPGA projects and significantly shortened the verification cycle. Research has shown that this method based on test coverage analysis can effectively solve key problems in FPGA verification, and has good practical value and promotion prospects.