Community search (CS), aiming to find a densely connected subgraph containing given query vertices, is an important research topic in social network analysis and is widely used in similar recommendation, team organization, friendship recommendation and other practical applications. The purpose of the CS system is to display searched community in a visual form to users. It can help users better understand and analyze networks, making better decisions. However, the exist CS systems are mostly designed for static graphs, they cannot capture the dynamic attributes and cannot intuitively display the dynamic changes of the community. In this paper, we develop a CS system over dynamic graph based on graph neural network (GNN), aiming to locate the community with cohesive attributes over dynamic graph and visualize the community to intuitively display the dynamic changes of vertices and the relationships between them. We design a GNN-based method to capture the dynamic changes of attributes and design a friendly front-end interface that visualizes the result community in the form of a timeline. It allows users to view the status of the result community at any snapshot and fine-tune the result community according to their own conditions.