Earthquake early warning systems are one of the effective means to mitigate earthquake disasters. In recent years, frequent earthquakes have caused enormous losses to human lives and property, making it extremely urgent to build a safe and reliable earthquake early warning system. The ShakeAlert system is an earthquake early warning system on the United States. West Coast. Since its official launch in 2019, it has now been updated to version 3.0. The monitoring network of the ShakeAlert system includes 1, 400 seismic stations, 1, 100 GNSS stations, as well as the MEMS, DAS, and BSM sensor networks under research. Its main algorithms consist of three seismic source algorithms: EPIC, FinDer, and GFAST-PGD, along with the Solution Aggregator (SA) module, Decision Module (DM), and ground motion estimation module (Eqinfo2GM). The operational process of ShakeAlert is closely linked: the data layer captures and transmits raw data, the production layer conducts collaborative analysis through multiple algorithms, and the alert layer conducts final review for release and dynamic updates, forming an efficient closed loop from perception to early warning. This paper studies its performance from the three dimensions of timeliness, accuracy, and reliability. The system can issue the first alert within 4 to 20 seconds in areas with dense stations, and successfully warned 41 out of 53 earthquakes with magnitude M≥4.5 from 2019 to 2023. However, the ShakeAlert system still has problems such as high system complexity, insufficient station distribution, shortcomings in algorithm performance, difficulty in identifying intensity anomaly zones, and failure of the public's scientific risk avoidance behaviors. As an internationally leading early warning system, ShakeAlert has important reference value for its technical experiences such as multi-source monitoring integration and dynamic algorithm weighting. In the future, China can improve the accuracy and social effectiveness of its earthquake early warning system by densifying stations in weak areas, optimizing algorithm fusion mechanisms, and strengthening public education.