With the advancement of global informatization process, the development of online recruitment enterprises shows a continuous growth trend. Moreover, the growth rate has long been higher than the average level of the information industry. The adjustment and improvement of industrial structure has become an important means for the sustainable development of online recruitment enterprises. In order to further improve the development level of enterprise online recruitment performance, this paper proposes an improved intuitionistic fuzzy analytic hierarchy process and further proposes an intuitionistic fuzzy TOPSIS method optimized by adaptive ant colony algorithm. Select the sample system and finally determine the index system. Finally, the performance of the improved intuitionistic fuzzy set and TOPSIS method is evaluated. The results show that the improved intuitionistic fuzzy set based on adaptive ant colony algorithm and TOPSIS method proposed in this paper is obviously superior to other methods in optimization ability, stability, convergence speed, and running time and can be better applied to practical work. The improvement of the average performance level of online recruitment enterprises in 2022 mainly depends on the improvement of recruitment and appointment level. Enterprises also need to strengthen recruitment and appointment and optimize the company's performance management as a whole.

Decomposing the structure of a large number of existing posts through data mining will greatly improve the effect of enterprise human resource structure optimization. To this end, this paper proposes an end-to-end competency-aware job requirement generation framework to automate the job requirement generation, and the prediction based on competency themes can realize the skill prediction in job requirements. Then an encoder-decoder LSTM is proposed to implement job requirement generation, and a competency-aware attention mechanism and a replication mechanism are proposed to guide the generation process to ensure that the generated job requirement descriptions comprehensively cover the relevant and representative competency and job skill requirements. A competency-aware strategy gradient training algorithm is then proposed to further enhance the rationality of the generated job requirement descriptions. Finally, extensive experiments on real-world HR data sets clearly validate the effectiveness and interpretability of the proposed framework and its variants compared to state-of-the-art benchmarks.

In smart elderly care communities, optimizing the design of building energy systems is crucial for improving the quality of life and health of the elderly. This study pioneers an innovative adaptive optimization design methodology for building energy systems by harnessing the cutting-edge capabilities of deep reinforcement learning. This avant-garde method initially involves modeling a myriad of energy equipment embedded within the energy ecosystem of smart elderly care community buildings, thereby extracting their energy computation formulae. In a groundbreaking progression, this study ingeniously employs the actor–critic (AC) algorithm to refine the deep deterministic policy gradient (DDPG) algorithm. The enhanced DDPG algorithm is then adeptly wielded to perform adaptive optimization of the operational states within the energy system of a smart retirement community building, signifying a trailblazing approach in this realm. Simulation experiments indicate that the proposed method has better stability and convergence compared to traditional deep Q-learning algorithms. When the environmental interaction coefficient and learning ratio is 4, the improved DDPG algorithm under the AC framework can converge after 60 iterations. The stable reward value in the convergence state is −996. When the scheduling cycle of the energy system is between 0:00 and 8:00, the photovoltaic output of the system optimized by the DDPG algorithm is 0. The wind power output fluctuates within 50 kW. This study realizes efficient operation, energy saving, and emission reduction in building energy systems in smart elderly care communities and provides new ideas and methods for research in this field. It also provides an important reference for the design and operation of building energy systems in smart elderly care communities.