To address the problem that the methods of cloud platforms to monitor application performance have a poor ability to collect and analyze anomalies in the whole process, this study proposes an application anomaly detection and bottleneck identification system based on cloud platform service components (AAD-PSC) that can provide monitoring and analysis characterized by customizable indicator values of applications on a cloud platform with multi-tier architecture. For this purpose, this system collects service invocation data at the front-end application service layer and correlates them with anomaly events. Then, customized anomaly detection methods are determined for the applications to achieve the optimal detection results. Finally, performance anomalies caused by non-workload changes are identified, and bottleneck identification is conducted. Experimental results show that the proposed monitoring system, able to quickly and accurately detect different types of anomaly events and identify corresponding performance bottlenecks, meets the needs of a cloud platform in application performance monitoring.