To discover correlations in massive telemetry data efficiently, a novel correlation knowledge discovery method based on the improved MIC (maximal information coefficient) was proposed. The Mini Batch K-Means clustering algorithm was used to discretize data in the precursor process; the mutual information between two variables under this partition was calculated and normalized by information entropy instead of maximal entropy to obtain the information coefficient; the MIC was selected as the measure of variable correlation. Aflerwards, the method was applied to the correlation analysis of the quantum satellite telemetry data, and the results show that the proposed method can effectively solve the problem of MIC measure bias to multivalued variables compared with the method based on dynamic programming algorithm, the time complexity dropped from O(n^2.4)to O(n^1.6), and it is an effective method for large-scale telemetry data correlation analysis.