An indexing method based on confusion network instead of Lattice is proposed in the weighted finite-state transducer framework (WFST) to improve the efficiency of the spoken term detection system. In the indexing stage, firstly confusion networks are extracted from Lattices and transformed to automatons; Then, timed factor transducers are constructed with these automatons; Finally, the index is achieved by taking the union of the factor transducers and optimizing the union. In the searching stage, the queries are transformed to automatons and then composed with the index. After optimization, the automaton representing the searching results is obtained. Experimental results show that compared with the WFST index based on Lattice, the confusion network-based index has smaller index size, faster searching speed and better performance when ensuring the retrieval accuracy.