[Objective] Average people usually do not know the complex associations among diseases, which poses negative effects to their health information seeking experience. This study tries to detect the associations among diseases using popular medical information with the help of deep learning technology (Word2Vec), aiming to improve personalized information services. [Methods] First, we identified 30 common disease topics with the help of medical professionals, and then collected related reports from Medical News Today. Second, we built word vector for each document with Word2Vec technology to calculate the semantic similarities among them. Finally, we compared the machine training results with experts’ scores to evaluate the performance of the proposed method. We also investigated the impacts of different models, optimization methods, data sizes and important parameters to the results. [Results] The correlation coefficient between the Word2Vec results and the experts’ scores reached 0.635 in optimal condition. We found that Skip-Gram model with less than 20 negative samples on large scale dataset yielded the best results. [Limitations] The precision of the Word2Vec judgment was affected by the number of disease topics. The granularity of disease topic needed to be improved. [Conclusions] The Word2Vec technology could be used to identify diseases association from consumer health information sources. It could also be used to improve the personalized health information services.