In Chinese word segmentation fields，the most widely used method is character-based tagging，which reformulates segmentation task to a sequence tagging task. The Conditional Random Fields (CRFs) tagger is the best tagger which can achieve state-of-the-art performance. The segmentation of the command orders is one of the basics of the auto-generation of command orders. Yet when using the model for command orders segmentation，problems of bad time and space efficiency are encountered. The model is analyzed and feature subsets are selected by using the feature selection algorithm，which cut the overhead of time and space effectively and improve the efficiency of the model. Then a novel post-process using CRFs confidence is presented to further improve performance. By combining the feature selection method and the confidence-based post-process，great improvement is achieved and the experimental results are satisfactory.