Among all kinds of time series classification algorithm, algorithms based on local features of time series data have achieved reasonable results. However, there is still abundant space for improvements of them in time complexity and accuracy. In this study, we propose an improved algorithm based on local features. It focuses on the property of local features and put restrictions on the set of local features. On the one hand, supported by theoretical analysis, our new algorithm cuts the size of set of local features and consequently reduces the time and space complexity. On the other hand, we redefine the criteria of selecting local features so that we can select more discriminative local features.