The widespread use of the Internet of Things (IoT) has also brought about issues such as fake information and threats to resource security. Trust management, by evaluating the trustworthiness of entities and the trust relationships between entities, aims to identify false messages, detect malicious nodes, and maintain network security. Compared to other security techniques, trust management places more emphasis on entity behavior and motives. This paper quantifies entity trustworthiness using weighted Beta distributions and detects malicious nodes based on this quantification. To comprehensively assess the trust status of entities, four conditions are designed to collect detailed information about the entities and their surrounding environment. Each condition forms a Beta distribution, and the weighted sum of these four conditions' distributions represents the entity's trustworthiness value. The paper also analyzes the weight allocation and resistance to malicious attacks of the proposed trust evaluation mechanism, providing corresponding countermeasures against attacks. Furthermore, simulation experiments are conducted to validate the effectiveness of the proposed solution. Compared to other trust evaluation mechanisms, the proposed approach considers a broader range of information about entities. Moreover, in practical applications, the types of conditions can be increased or decreased to accommodate complex and dynamic environments.