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Accurately assessing the health status of power plant equipment is of great significance to guaranteeing the safe and stable production of power plants and improving the safety of equipment operation. Aiming at the problem that the current power plant equipment health assessment method has low prediction accuracy, an intelligent power plant equipment health assessment method based on the integrated deep random forest algorithm is proposed. Firstly, the structure of the power plant equipment health assessment system is introduced in detail, and the health assessment data structure and factors influenced are analyzed. Secondly, the equipment evaluation is divided into six different levels, which makes the equipment health analysis more convenient. Then, combined with deep learning and ensemble learning technology, an integrated deep random forest algorithm is proposed. Finally, the effectiveness of the proposed method is verified by simulation experiments. The results show that the proposed method improves the accuracy of the evaluation model.