A video recommendation method, comprising: acquiring identifier information of a watched video, position information of the watched video, identifier information of a current video, and position information of the current video; inputting the identifier information of the watched video and the position information of the watched video to a first model to obtain a preference vector of the watched video, wherein the position information of the watched video refers to position information of the watched video on a display page and/or a display page where the watched video is located; and inputting the preference vector of the watched video, the identifier information of the current video, and the position information of the current video to a second model to obtain a preference vector of the current video, wherein the position information of the current video refers to position information of the current video on a display page and/or a display page where the current video is located; and determining a target video according to the preference vector of the current video, by: inputting a preference vector corresponding to a first current video to each of N expert networks, wherein a proportion of the preference vector corresponding to the first current video in each expert network is different, a being-selected weight corresponding to each expert network is different, and N is a positive integer greater than or equal to 1; outputting a target preference vector corresponding to the first current video according to the proportion of the preference vector corresponding to the first current video in each expert network and the being-selected weight corresponding to each expert network; obtaining N target preference vectors corresponding to N current videos; and determining N target videos according to the N target preference vectors. acquiring identifier information of a watched video, position information of the watched video, identifier information of a current video, and position information of the current video; inputting the identifier information of the watched video and the position information of the watched video to a first model to obtain a preference vector of the watched video, wherein the position information of the watched video refers to position information of the watched video on a display page and/or a display page where the watched video is located; and inputting the preference vector of the watched video, the identifier information of the current video, and the position information of the current video to a second model to obtain a preference vector of the current video, wherein the position information of the current video refers to position information of the current video on a display page and/or a display page where the current video is located; and determining a target video according to the preference vector of the current video, by: inputting a preference vector corresponding to a first current video to each of N expert networks, wherein a proportion of the preference vector corresponding to the first current video in each expert network is different, a being-selected weight corresponding to each expert network is different, and N is a positive integer greater than or equal to 1; outputting a target preference vector corresponding to the first current video according to the proportion of the preference vector corresponding to the first current video in each expert network and the being-selected weight corresponding to each expert network; obtaining N target preference vectors corresponding to N current videos; and determining N target videos according to the N target preference vectors. inputting a preference vector corresponding to a first current video to each of N expert networks, wherein a proportion of the preference vector corresponding to the first current video in each expert network is different, a being-selected weight corresponding to each expert network is different, and N is a positive integer greater than or equal to 1; outputting a target preference vector corresponding to the first current video according to the proportion of the preference vector corresponding to the first current video in each expert network and the being-selected weight corresponding to each expert network; obtaining N target preference vectors corresponding to N current videos; and determining N target videos according to the N target preference vectors.