Crowdsourcing, which decomposes tasks or projects through the internet platform and outsources them to a large number of people, has gained significant academic and industry interest due to its efficiency. Although prior theoretical studies have employed evolutionary game theory to study crowdsourcing systems, existing studies have predominantly focused on the game interactions between crowdsourcing worker and requester, often neglecting the platform as a stakeholder. Here we propose an evolutionary game model for tripartite crowdsourcing, accounting for bounded rationality and potential collusion between platform and crowdsourcing worker, with incentives designed to counteract free-riding and deceptive feedback. Our theoretical results indicate that the system can achieve a desired state where worker is certain to choose to provide high-quality work, platform is certain to implement diligent monitoring, and requester is certain to adopt an integrity strategy. Moreover, when the platform consistently adopts the monitoring strategy, the system exhibits oscillatory dynamics, with the probabilities of the worker providing high-quality work and the requester adopting an integrity strategy fluctuating within a bounded interval. Finally, we present numerical examples to validate our theoretical results.