Screen Content Coding (SCC) is an extension of the High-Efficiency Video Coding (HEVC) for encoding screen content videos. However, there are many legacy screen content videos already encoded by HEVC. To efficiently migrate screen content videos from the existing HEVC to the emerging SCC, a machine learning based fast transcoding algorithm is proposed by using decision trees in this paper. To speed up the transcoding process, the intermediate data from both the HEVC decoder side and the SCC encoder side are jointly analyzed. Then the optimal coding unit (CU) sizes are mapped from HEVC to SCC while the mode candidates are adaptively checked according to the decision tree outcomes in the re-encoding process. Experimental results show that an average of 48.20% re-encoding time reduction is achieved with only 1.47% Bjontegaard delta bitrate loss using All Intra (AI) configuration.