The IOT management platform is used to handle and transmit data from many types of power system terminal devices. The current IOT management platform has a low data processing efficiency and a high mistake rate when it comes to finding anomalous data. Furthermore, the effective selection and optimum decision of the convolutional neural network’s structural parameters has a significant impact on prediction performance. Based on this, the paper proposes a decision algorithm for locating anomalous data in an IOT integrated management platform using a convolutional neural network (CNN) and a global optimization decision of key structural parameters of a convolutional neural network using an improved particle swarm optimization (APSO) algorithm. First, an index model is created to determine if the data retrieved from the IOT management platform is anomalous or not. Second, the structure of the convolutional neural network-based decision method for finding anomalous data is examined. Following that, an enhanced particle swarm optimization technique is developed to optimize the structural parameters of the convolutional neural network, and an APSO-CNN with improved performance for anomalous data localization is generated. Finally, the established algorithm’s correctness, feasibility, and efficacy were evaluated using the Adam optimizer. The results reveal that the established APSO-CNN-based decision algorithm for anomaly data localization offers considerable benefits in terms of accuracy and running time, with extremely interesting application potential.