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In view of the insufficiency of big data application technology in medical insurance information construction, this study takes the medical insurance special disease settlement data of Suzhou Industrial Park as the analysis object, and combs, analyzes and cleans the big data in medical insurance informationization construction through big data algorithm. , reconstruction, etc., and then build a moving average, exponential average model to achieve the processing of big data. This study also uses the random matrix theory algorithm to realize the energy spectrum and eigenstate analysis and statistics of medical data, and obtain the random degree in the actual measurement, revealing the characteristics of the overall associated event included in the medical insurance informationization construction big data, and using the data. The mining algorithm again reprocesses the analyzed data, so that users can quickly get from massive data (such as uremia, cataract, aplastic anemia, hemophilia, malignant tumor rehabilitation, coronary heart disease with myocardial infarction, epilepsy) Demand target data to achieve classification and analysis of data. The realization of the data shows that this research method has obvious practical value, and provides technical reference for the sustainable development of medical insurance fund and the formulation and improvement of medical insurance policy.