  The recommendation algorithm based on knowledge graphs is at a relatively
mature stage. However, there are still some problems in the recommendation of
specific areas. For example, in the tourism field, selecting suitable tourist
attraction attributes process is complicated as the recommendation basis for
tourist attractions. In this paper, we propose the improved Attention Knowledge
Graph Convolution Network model, named ($Att-KGCN$), which automatically
discovers the neighboring entities of the target scenic spot semantically. The
attention layer aggregates relatively similar locations and represents them
with an adjacent vector. Then, according to the tourist's preferred choices,
the model predicts the probability of similar spots as a recommendation system.
A knowledge graph dataset of tourist attractions used based on tourism data on
Socotra Island-Yemen. Through experiments, it is verified that the Attention
Knowledge Graph Convolution Network has a good effect on the recommendation of
tourist attractions and can make more recommendations for tourists' choices.

Information Retrieval;Artificial Intelligence;Graphics;Machine Learning