Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not appli-cable to a heterogeneous network. Registered patent history data denotes the innovations and the achievements in different re-search fields. In this paper, we present an iteration algorithm called inventor-ranking, to sort the influences of patent inventors in heterogeneous networks constructed based on their patent data. This approach is a flexible rule-based method, making full use of the features of network topology. We sort the inventors and patents by a set of rules, and the algorithm iterates continuously until it meets a certain convergence condition. We also give a detailed analysis of influential inventor’s interesting topics using a latent Dirichlet allocation (LDA) model. Compared with the traditional methods such as PageRank, our approach takes full ad-vantage of the information in the heterogeneous network, including the relationship between inventors and the relationship be-tween the inventor and the patent. Experimental results show that our method can effectively identify the inventors with high influence in patent data, and that it converges faster than PageRank.