A kind of noncoding RNA with length more than 200 nucleotides named long noncoding RNA (lncRNA) has gained considerable attention in recent decades. Many studies have confirmed that human genome contains many thousands of lncRNAs. LncRNAs play significant roles in many important biological processes, including complex disease diagnosis, prognosis, prevention and treatment. For some important diseases such as cancer, lncRNAs have been novel candidate biomarkers. However, the role of lncRNAs in human diseases is still in its infancy, and only a small part of lncRNA-disease associations have been experimentally verified. Predicting lncRNA-disease association is an important way to understand the mechanism and function of lncRNA involved in diseases to enrich the annotations of lncRNA. Therefore, it is urgent to prioritize lncRNAs potentially associated with diseases. Biological system is a highly complex heterogenous network involved different molecules. Therefore, the algorithms based on network methods have been extensively applied in information fields which can provide a quantifiable characterization for the networks characterizing multifarious biological systems. A heterogeneous network topology possessing abundant interactions between biomedical entities is rarely utilized in similarity-based methods for predicting lncRNA-disease associations based on the array of varying features of lncRNAs and diseases. DeepWalk, encoding the relations of nodes in a continuous vector space, is an extension of language model and unsupervised learning from sequence-based word to network. In this article, we present a novel lncRNA-disease association prediction method based on DeepWalk, which enhances the existing association discovery methods through a topology-based similarity measure. We integrate the heterogeneous data to construct a Linked Tripartite Network which is a heterogeneous network containing three types od nodes which generated from bioinformatics linked datasets and use DeepWalk method to extract topological structure features of the nodes in the linked tripartite network for calculating similarities. Our proposed method can be separated into the following steps: Firstly, we integrate heterogeneous data to construct a Linked Tripartite Network: containing the topological interactions of known lncRNA-disease, lncRNA-microRNA and microRNA-disease. Secondly, the topological structure features of the nodes are extracted based on DeepWalk. Thirdly, similarity scores of disease-disease pairs and lncRNA-lncRNA pairs are computed based on the topology of this network. Finally, new lncRNA and disease associations are discovered by rule-based inference method with lncRNA-lncRNA similarities. Our proposed method shows superior predictive performance for prediction of lncRNA-disease associations based on topological similarity from heterogenous network. The AUC value is used to show the performance of our method. The similarity measurement using network topology based on DeepWalk provide a novel perspective which is different from the similarity derived from sequence or structure information. Availability: All the data and codes are freely availability at: https://github.com/Pengeace/lncRNA-disease-link. Copyright © 2019 Elsevier Inc. All rights reserved.