Abstract: Patent value refers to the exchange value of a patent in the purchase and transaction.It can provide precious information for the decision-makings of patent owners and buyers.Existing patent value evaluation methods are mainly based on training or citation analysis.However, the methods based on training depend too much on the experimental parameters, which results in weak credibility.On the other hand, the methods based on citation analysis only consider direct citations during the evaluation leaving indirect citations and novelty of patent neglected.For these reasons, this paper presents a latent-citation-network based patent value evaluation method to evaluate the value of each patent in which direct citations, indirect citations and novelty are all considered.First, the latent citation association is discovered utilizing similarity between patents and a latent citation network is established.Then, a basic algorithm is implemented to effectively evaluate the value of each patent on the network.Further, an improved algorithm is proposed to solve the problem of inefficiency of the basic algorithm.Finally, to handle the value variations caused by the arrival of a new patent, a dynamic patent value evaluation algorithm is designed to efficiently update the value of the original patents.As shown in the experiments, the proposed methods in this paper are effective.