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Modern satellites have gradually become a major national infrastructure. In order to understand satellite on orbit working status, it is necessary to analyze the telemetry data. The fast-changing telemetry data contains a large amount of satellite service information. The analysis and modeling of the data based on machine learning algorithm can make better use of the fast-changing telemetry data with high feature dimension and large amount data, and provide a possible scheme for artificial intelligence in satellite modeling, operation and maintenance. A method of modeling the fast-changing telemetry data of on orbit satellite based on random forest algorithm is proposed, and an improved dual grid search method is introduced to optimize the model parameters. The model is used to predict the power measurement value of a frequency point. The results show that the R2 value is more than 0.98 and the error of the prediction value is small. A fast-changing telemetry data model with good effect is established, which provides a possible scheme for the analysis of fast-changing telemetry data based on machine learning.