The existing similarity measurement methods for stock time series always ignore the trading volume and other important factors influencing the stock price. This phenomenon results in inaccuracy when clustering and classifying the series. To solve the problem, a new similarity measurement method for stock time series is proposed. The method which is based on dynamic time warping(DTW） introduces time-exhaustion factor and trading volume factor, and puts forward the ultimate similarity measurement formula for stock time series. To prove the feasibility and validity of the method, the stock time series in the household appliances and two others in the experiment of this paper are tested. The test result indicates that the new similarity measurement method based on DTW can maintain the shape features of stock series. On this basis, the method can solves the problem of price-volume relationship in the stock technical analysis well. Thus the method can be applied to pattern discovery and other fields in the stock technical analysis for more effective results.