Person-job matching model combining Bert and attention mechanisms

In recent years, artificial intelligence has been widely used in the management and services of smart cities due to its ability to simulate human intelligent behavior and process large amounts of data. Smart city management and services cover all aspects of society, including but not limited to employment services and traffic management. In these areas, employment services are a key component of smart city services, especially in terms of improving urban employment rates, and the effectiveness of person-post matching is particularly important. Aiming at the problem of low accuracy of the current person-job matching methods, the study firstly constructed a knowledge graph of supply and demand based on Internet recruitment. Subsequently, the method of combining the fusion two-way transformer model and the attention mechanism is proposed, and the method is applied to the knowledge graph to construct a person-job matching model that combines the two-way transformer model and the attention mechanism. Finally, the person-post matching model is subjected to performance analysis, and the outcomes denote that the matching accuracy and checking accuracy of the model are 98.7% and 97.6%, respectively, which are better than the comparison model. The above results show that the model proposed in the study has good performance and effectiveness, which helps to improve the accuracy of person-post matching.