In order to effectively solve the problems such as insufficient monitoring methods and detection difficulties for existing collusive behaviors in construction project bidding and tendering and to enhance the identification efficiency and preventive capabilities for such behaviors, an intelligent monitoring and early warning system for collusive behaviors in construction project bidding is established based on big data and artificial intelligence technologies. The study results show that the system adopts the hierarchical progressive architecture design (consisting of a data acquisition layer, analysis processing layer, and decision support layer) and can rapidly identify the collusive behaviors by organically integrating multi-source heterogeneous data and establishing a cross-model joint recognition model, in combination with distributed computing advantages. At the same time, by integrating the rapid screening function of a rule engine and the in-depth analysis capability of ensemble learning algorithms, a risk-graded early warning response mechanism for collusion was established in this system. Through a visual interface, the early warning results were visually displayed and the auxiliary decision-making was supported. This system has successfully promoted the transformation of collusive behavior in project tendering and bidding from passive investigation and tracking to proactive prevention, significantly improved the efficiency of tendering and bidding supervision, standardized the market competition order, and optimized the allocation of public resources. The findings of this study can provide technical support and tool references for the intelligent monitoring and early warning of collusive behaviors in the project construction bidding, and have significant practical value for promoting the intelligent development of industry supervision.