Optimizing network structure to promote information propagation has been a key issue in the research field of complex network, and both clustering and diffusion characteristics of edges in a network play a very important role in information propagation. K-truss decomposition is an algorithm for identifying the most influential nodes in the network. We find that K-truss decomposition only considers edge clustering characteristics, without considering the diffusion characteristics, so it is easily affected by the local clustering structure in the network, such as core-like groups. There are mutually closely connected the core-like groups in the network, but the correlation between the core-like groups and the other parts of the network is less, so the information is easy to spread in the core-like groups, but not in the other parts of the network, nor over the whole network. For the reason, we propose an index to measure the edge diffusion characteristics in a network, and it is found that the diffusion characteristics of some edges in the periphery of the network are relatively high, but the clustering characteristics of these edges are relatively low, so they are not beneficial for rapid information propagation. In this paper, by considering the relationship between the clustering characteristics and diffusion characteristics of the edges, we propose a novel network structure optimization algorithm for information propagation. By measuring the comprehensive ability strength of the clustering characteristics and the diffusion characteristics of the edges, we can filter out the edges whose comprehensive ability is poor in the network, then determine whether the edges should be optimized according to the relative relationship between the clustering characteristics and the diffusion characteristics of the edges. To prove the effectiveness of the proposed algorithm, it is carried out to optimize the structures of four real networks, and verify the effective range of information propagation before and after the optimization of network structure from the classical independent cascade model. The results show that the network topology optimized by the proposed algorithm can effectively increase the range of information propagation. Moreover, the number of leaf nodes in the optimized network is reduced, and the clustering coefficient and the average path length are also reduced.