This paper presents an innovative self-decision model aimed at mitigating hardware faults within cloud platforms. The model leverages the SBERT pre-training model to extract unbiased feature vectors from hardware fault alarm data, which are then utilized as inputs for training the XGBoost model. Subsequently, a hardware self-decision system is implemented for cloud platforms based on this model, facilitating automated decision-making and recovery from hardware faults. Comparative experiments indicate that the decision-making accuracy achieved by the XGBoost classification model outperforms that of fault processing decision-making accuracy achieved through other machine learning methods. By amalgamating natural language processing technology and machine learning algorithms within the realm of cloud platform hardware fault processing, this methodology substantially reduces the time and resources necessitated by engineers to address hardware fault alarms from diverse vendors. The experimental results corroborate the efficacy of the proposed approach in augmenting the accuracy and efficiency of fault handling decisions.