In order to reduce the redundancy in the micro-motion feature set of aerodynamic targets and reduce the feature dimensionality， random forest is introduced to evaluate the importance of multi-dimensional micro-motion features. The random forest algorithm and project implementation process are described， and 18 micro-motion features in the time domain， frequency domain and time-frequency domain are extracted. VHF band measured data are used to verify the feature importance evaluation algorithm based on random forest， and the influence of feature selection on the performance of tree classifiers including Fisher， support vector machine and decision tree classifier， as well as the influence of the number of radar coherent accumulation pulses on the feature importance evaluation are analyzed. For Fisher and support vector machine， with the increase of feature dimension， classification accuracy can be improved slightly. For decision trees， classification accuracy is only affected by features with higher importance score.