In Internet Protocol Television （IPTV） applications， a television terminal is usually shared by several family members. The exiting recommendation algorithms are difficult to analyze the different interests and preferences of family members from the historical data of terminal. In order to meet the video-on-demand requirements of multiple members under the same terminal， a capsule network-based IPTV video-on-demand recommendation model， namely CapIPTV， was proposed. Firstly， a user interest generation layer was designed on the basis of the capsule network routing mechanism， which took the historical behavior data of the terminal as the input， and the interest expressions of different family members were obtained through the clustering characteristic of the capsule network. Then， the attention mechanism was adopted to dynamically assign different attention weights to different interest expressions. Finally， the interest vector of different family members and the expression vector of video-on-demand were extracted， and the inner product of them was calculated to obtain the Top-N preference recommendation. Experimental results based on both the public dataset MovieLens and real radio and television dataset IPTV show that， the proposed CapIPTV outperforms the other 5 similar recommendation models in terms of Hit Rate （HR）， Recall and Normalized Discounted Cumulative Gain （NDCG）.