With the progress of smart grid, power systems are faced with more and more complex data challenges, including large-scale data, multi-sample types and low resource integration. This research takes power big data as the object, carries out abnormal power consumption behavior detection and load prediction based on Hadoop platform, and successfully realizes the visualization processing of power data. We thoroughly analyzed the needs of power big data visualization and designed the overall Hadoop-based architecture. In terms of key technologies, we adopt the visualization method based on geographic information, and use MapReduce to carry out parallel computation, and effectively identify and display different electrical behaviors. In addition, we proposed a wavelet neural network power load forecasting method based on FCM, and personalized classification of power users through FCM clustering. Combined with the prediction ability of wavelet neural network, accurate power load prediction was successfully realized, and parallel calculation was carried out on the distributed platform to further improve the prediction accuracy. Finally, we built a Hadoop big data experiment platform to verify the experimental results and performance of the proposed method. The experimental results show that our research has the ability to effectively cope with the big data challenges of power system, and provides important support and guidance for the safe and stable operation of power system.