The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually based on statistical computation, which can no longer meet the processing needs of massive data and do not take into account the hidden characteristics of data, which can easily lead to the problem of information scarcity. The paper combines recurrent convolutional neural network and traditional human resource allocation algorithm and designs a double recurrent neural network job matching recommendation algorithm applicable to the human resource field, which can improve the traditional algorithm data training quality problem. In the experimental part of the algorithm, the arithmetic F1 value in the paper is 0.823, which is 20.1% and 7.4% higher than the other two algorithms, respectively, indicating that the algorithm can improve the hidden layer features of the data and then improve the training quality of the data and improve the job matching and recommendation accuracy.

The management and development of human resources (HR) have become one of the core contents of enterprise management. Enterprises pay more attention to the importance of information technology which results in the development of many labor dispatch enterprises. The rapid development and limited market share of labor dispatch enterprises lead to more fierce competition among the enterprises. With the rapid development of machine learning techniques, more enterprises begin to pay attention to managing the completion of various decision-making activities in operation with the help of computer information systems to save human costs. The labor dispatch model is a valuable addition to China's core contractual employment paradigms. Based on the improved genetic algorithm, this paper studies the human resource labor dispatch model which is used to optimize the number of people required by the dispatch project and optimize its labor cost. The HR recruitment model is used to calculate the number of people to be recruited for the dispatch project to meet the needs of the project and optimize its project dispatch scheme. The experimental results show that the benefit of dispatching through the artificial selection method (1127) is less than the result calculated by the HR dispatching plan model (1162), which proves the effectiveness of this model. The experimental results of the study depict the usefulness of the research.