In big data environment, analyzing the historical data of music and audiences can achieve accurate prediction of music popular trends. Based on the STL and HoltWinters decomposition models, a Time Series based Music Prediction (TSMP) algorithm is proposed. The TSMP algorithm analyzes data from both longterm trends and periods,codes and categorizes the longterm trends, and uses the category optimal value selection method to predict the music popular trends.Based on the TSMP algorithm, the Extend TSMP (ETSMP) algorithm is proposed, which is based on the subsequence pattern matching method and the additional processing of the newly released new album to achieve more accurate prediction.In the “2016 Chinese University Computer ContestBig Data Challenge Ali music popular trends prediction competition” hosted by Tsinghua University and Tianchi big data competition platform of Ali cloud, the participating team uses the proposed ETSMP algorithm to achieve good prediction for artist’s play times from september to october in 2016 and won the second place in this competition.