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Spacecraft telemetry data is a direct manifestation of spacecraft status. Continuous in-depth analysis and research on spacecraft telemetry data can provide effective guarantees for the safety and stability of spacecraft. At present, the telemetry data of complex spacecraft has problems such as large amount of test data, low manual interpretation efficiency, complex associations between data, and difficult to sort out. At the same time, the level of intelligent data analysis is low, and the effective use of massive historical test data is lacking. In order to overcome the shortcomings of the existing technology, by researching the association rules mining method of spacecraft telemetry data, the association rule mining algorithm based on state transition extraction is proposed, and conduct experimental mining and comparative analysis on the FP-Growth algorithm, implement the mining and analysis of the association rules of spacecraft telemetry data parameters, and effectively solve the problem of combing the association rules among the spacecraft telemetry data. The test results are highly accurate and provide important reference for mining the association rules of spacecraft operating conditions and parameters.