Data mining-based scenarios in construction engineering management have become the mainstream, but in the actual application process, big data mining is still affected by many factors, and some construction enterprises have not realized the importance of big data mining for project management. In this paper, aiming at the problems existing in the quality evaluation of construction projects, this paper combines the analytic hierarchy process and entropy (entropy) to carry out joint weighting, and uses the fitting algorithm of support vector machine and BP neural network to form an expert decision-making model, which is applied to practical work. In view of the current construction engineering quality evaluation results are not objective and low credibility, quality of construction engineer model based on genetic algorithm is proposed. Finally, the genetic algorithm is used to optimize the parameters of the construction quality evaluation model, and the model is applied to the specific construction quality evaluation. The results show that the accuracy of the model is higher than that of other models, and the evaluation results of construction engineering quality are more credible and have strong practical application value.