To address the problem that the development of machine learning requires a large number of real datasets with both data security and availability， an improved K-anonymity privacy protection algorithm based on Random Forest （RF） was proposed， namely RFK-anonymity privacy protection. Firstly， the sensitivity of each attribute value was predicted by RF algorithm. Secondly， the attribute values were clustered according to different sensitivities by using the k-means clustering algorithm， and the data was hidden to different degrees by using the K-anonymity algorithm according to the sensitivity clusters of attribution. Finally， data tables with different hiding degrees were selected by different users according to their needs. Experimental results show that in Adult datasets，compared with the data processed by K-anonymity algorithm， the accuracies of the data processed by the RFK-anonymity privacy protection algorithm are increased by 0.5 and 1.6 percentage points at thresholds of 3 and 4， respectively； compared with the data processed by （p，α， k）-anonymity algorithm， the accuracies of the data processed by the proposed algorithm are improved by 0.4 and 1.9 percentage points at thresholds of 4 and 5. It can be seen that RFK-anonymity privacy protection algorithm can effectively improve the availability of data on the basis of protecting the privacy and security of data， and it is more suitable for classification and prediction in machine learning.