Entity resolution widely exists in data tasks such as data quality control, information retrieval, and data integration. Traditional entity resolution methods mainly focus on relational data, while with the development of big data technology, the application requirements of cross-modal data are generated due to the proliferation of different modal data including texts and images. Hence, cross-modal data entity resolution has become a fundamental problem in big data processing and analysis. In this study, the research development of cross-modal entity resolution is reviewed, and its definition and evaluation indexes are introduced. Then, with the construction of inter-modal relationships and the maintenance of intra-modal relationships as the main line, existing research results are surveyed. In addition, widely used methods are tested on different open datasets, and their differences and reasons behind them are analyzed. Finally, the problems in the present research are concluded, on the basis of which the future research trends are given.