In recent years, new energy vehicles, as a high-tech industry, have developed rapidly. This paper uses “number of new energy project personnel” and “hours of R&D (research and development) personnel” as design indicators to evaluate the investment of innovative talents in enterprises. This paper first introduces the supporting factors of the innovation environment in the input of innovation resources, and conducts research from four perspectives: human resources, innovation R&D, technology acquisition, and environmental support. In the construction of the innovation output index system, this paper outlines that the technological innovation (TI) achievements of enterprises are related to factors such as technological capabilities, profits, and market competitiveness of enterprises. Finally, this paper evaluates it from three aspects: the research and development achievements, the economic benefits obtained and the competitive benefits of the enterprise. The results show that from 2018 to 2022, the average technological innovation efficiency of new energy enterprises is 1.06; TI’s efficiency indicators in the past five years are all above 1, and the overall improvement trend of TI is relatively stable. The new energy vehicle collaborative innovation system constructed in this paper will promote the overall development of the new energy vehicle industry.

This research proposes a hybrid improved marine predator algorithm (IMPA) and deep gated recurrent unit (DGRU) model for profit prediction in financial accounting information systems (FAIS). The study addresses the challenge of real-time processing performance caused by the increasing complexity of hybrid networks due to the growing size of datasets. To enable effective comparison, a new dataset is created using 15 input parameters from the original Chinese stock market Kaggle dataset. Additionally, five DGRU-based models are developed, including chaotic MPA (CMPA) and the nonlinear MPA (NMPA), as well as the best Levy-based variants, such as the dynamic Levy flight chimp optimization algorithm (DLFCHOA) and the Levy-base gray wolf optimization algorithm (LGWO). The results indicate that the most accurate model for profit forecasting among the tested algorithms is DGRU-IMPA, followed by DGRU-NMPA, DGRU-LGWO, DGRU-DLFCHOA, DGRU-CMPA, and traditional DGRU. The findings highlight the potential of the proposed hybrid model to improve profit prediction accuracy in FAIS, leading to enhanced decision-making and financial management.

No abstract