Considering the importance of energy in our lives and its impact on other critical infrastructure, this document begins with the full lifecycle of big data and breaks down risk factors on the security and privacy of big data into 5 stages: data collection, data transmission, storage, data use and data destruction. Integrated into the consideration of the cloud environment, this document analyzes the risk factors of each stage in detail and establishes a system of data energy security and confidentiality risk assessment metrics. According to different degrees of risk impact, the AHP method is used to give weights to the indices, a genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, then optimized weights and thresholds are assigned to the BP Neural Network and the evaluation samples in the database are used to train it. The trained model is then used to evaluate a case to verify the applicability of the model.