Many realistic wireless social networks are evolving over time. While network evolution has its important influence on network performances, it is nevertheless overlooked in most existing studies on information diffusion. Motivated by this, in this paper, we investigate the delivery accuracy of interest-aware information diffusion in evolving social networks. In doing so, we adopt a model, named affiliation networks, to characterize network evolution from three aspects, i.e., the arrival of new users, the generation of new interests, and the creation of new links between them. Based on that, we consider a publishing based information diffusion mechanism that widely exists in wireless networking services such as Facebook, Twitter, and Sina Weibo, where a user receives data items from his friends and then republishes the ones he is interested in to all his friends. Under the above network model, we study how the performance metric such as delivery accuracy is affected by the network evolution. The publishing based information diffusion mechanism is a blind targeting one that may suffer a low delivery accuracy. However, our analytical results demonstrate a contrary finding that the delivery accuracy is improved over time, and even more surprisingly, we disclose that with a sufficiently long evolving time, the delivery accuracy can achieve a perfect state where those who receive the data are exactly the ones that are interested in it. In addition, our theoretical findings are verified by experimental measurements through a social network dataset from Facebook.