Abstract: With the acceleration of the urbanization process in China, the population and wealth become highly concentrated, and cities tend to be larger and more complex, which makes them more vulnerable to earthquakes. What is worse, most of the cities in China are unfortunately located in the earthquake-prone regions, making the risk of earthquake disaster rise rapidly. It has become a top priority of China’s earthquake disaster mitigation work to carry out the research on earthquake risk assessment and monitoring technology by making full use of the international advanced ideas on seismic disaster risk mitigation and combining today’s intelligence technology. The national key research and development project entitled “Research on regional and urban earthquake risk assessment and monitoring technology” was initiated to establish the dynamic evaluation index system, assessment technology and software platform for the regional and urban major earthquake risk by developing high-performance regional and urban seismic disaster monitoring, networking and observation technologies. In this project, demonstration applications are carried out to realize scientific, accurate and dynamic evaluation on the region and urban earthquake risk, and to provide key technical support for significantly improving China’s capability to deal with the earthquake disaster risk. After the two years research, the MEMS accelerometer samples have been designed and produced, and the optimal method for arranging the observation network, optimizing arrangement of accelerometers for typical structure and the improved multi-hop routing algorithm for data transmission have been presented. The convolutional neural network Damage-Net for identifying the visual damage of RC members was established, a strong tracking filter algorithm was adopted for effectively tracking the time-varying physical parameters of the building structural system, and the seismic resilience assessment method of buildings was developed. A data anomaly detection method based on computer vision was proposed. A bridge damage index model based on difference rate of the elastic-plastic energy dissipation and a bridge damage identification method based on convolutional neural network and recursive plots have been put forward. Benchmark model of bridge seismic damage monitoring and performance evaluation has been established. The building information extraction technology based on the remote sensing data, recovery function model for the structural performance of both the individual building and regional buildings, and the method to quantify the structural resilience have been proposed. The index system and dynamic risk model have been developed for evaluating the regional and urban seismic disaster risk subjected to major earthquakes. The framework of the internet of things based on earthquake disaster monitoring system and the bridge parametric earthquake risk assessment model taking multiple damage states into account have been proposed. Earthquake disaster simulation system for buildings has been developed. The schematic design for the seismic monitoring system of the demonstration building has been preliminarily completed. The construction of the seismic monitoring network of the demonstration bridge has been accomplished. The multivariate information of Sanhe city has been collected and its database has been constructed. The earthquake disaster monitoring network has been preliminarily designed for the Sanhe region.