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.

基于数据挖掘的场景已成为建筑工程管理中的主流，但在实际应用过程中，大数据挖掘仍然受到诸多因素的影响，一些建筑企业尚未意识到大数据挖掘对项目管理的重要性。针对建设工程质量评价中存在的问题，结合层次分析法和熵（熵）法进行联合加权，并采用支持向量机和BP神经网络的拟合算法进行建模。一种专家决策模型，可应用于实际工作。针对目前建筑工程质量评价结果不客观，可信度低的缺点，提出了基于遗传算法的建筑工程模型质量评价。最后，利用遗传算法对施工质量评价模型的参数进行优化，并将该模型应用于具体的施工质量评价。结果表明，该模型的准确性高于其他模型，建筑工程质量评价结果更加可信，具有较强的实际应用价值。