Focusing on the issue that shapelets candidates can be very similar in time series classification by shapelets transform, a diversified top-k shapelets transform method named DivTopKShapelet was proposed. In DivTopKShapelet, the diversified top-k query method was used to filter similar shapelets and select the k most representative shapelets. Then the optimal shapelets was used to transform data, so as to improve the accuracy and time efficiency of typical time series classification method. Experimental results show that compared with clustering based shapelets classification method (ClusterShapelet) and coverage based shapelets classification method (ShapeletSelction), DivTopKShapelet method can not only improve the traditional time series classification method, but also increase the accuracy by 48.43% and 32.61% at most; at the same time, the proposed method can enhance the computational efficiency in 15 data sets, which is at least 1.09 times and at most 287.8 times.