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Aiming at the impact of the hardware failure of server bottom business on the stable operation of the system, a hybrid meta-heuristic optimization algorithm combining improved quantum behavior particle swarm optimization (QPSO) and genetic algorithm (GA) was proposed to train the parameters of adaptive neural fuzzy reasoning system (ANFIS). To obtain more accurate ANFIS rules for hardware fault warning. First, by analyzing the mapping relationship between server business and hardware related parameters, the ANFIS model is trained to construct the prediction model through the collected data set. Secondly, considering the problem that ANFIS is prone to fall into the local optimal value in the gradient calculation process, an IQPSO algorithm is designed to search ANFIS rule parameters globally by combining the crossover and mutation operators in GA mixed meta-heuristic algorithm. Finally, a set of post-processing sample data sets were used to test the effectiveness and stability of the proposed method. Experimental results show that the proposed method can effectively warn server hardware failures. The ANFIS model based on the proposed hybrid element heuristic optimization algorithm has faster convergence speed and higher global search accuracy, and the generalization accuracy is more than 47% higher than the traditional ANFIS model.