Yanqing He

As the main representative of new energy, photovoltaic energy is widely used in civil engineering construction, its purpose is to reduce the energy consumption in the project, there is a deviation in the integration of new energy and civil engineering, and it cannot effectively play its own advantages. Continuously analyze the changes in the energy consumption values of civil engineering. Then, the influencing factors of building energy consumption were introduced, and the covariance matrix was constructed, and the engineering construction benefit was taken as the ultimate goal. When calculating energy consumption, the dual benefits of building benefits are fully considered, and environmental protection and engineering energy consumption are estimated to reduce the error of energy consumption calculation. The results show that new energy can reduce the high energy consumption value in the project, improve the light transmittance, enhance the conversion rate of sunlight, and provide support for the transportation, lighting and thermal insulation of the building, with an increase rate of 10~25%, and the new energy can reduce the cost of civil engineering, with a reduction rate of 19~26%, and improve the economic and social benefits, with an increase of more than 10%. Therefore, new energy can reduce the energy and energy consumption of civil engineering, and expand the scope and application depth of new energy development.

In the field of civil engineering education, accurately evaluating the effectiveness of budget courses is crucial. However, traditional methods of evaluation tend to be cumbersome and subjective. In recent years, machine learning technology has demonstrated immense potential in educational evaluation. Nevertheless, in practical application, the machine learning-based evaluation model for civil engineering budget courses faces the predicament of inadequate evaluation accuracy. To solve this problem, the squirrel search algorithm technology was used to establish support vector machine parameters and create optimization algorithms. The performance of the proposed optimization algorithm was tested, and the results showed that the accuracy of the proposed algorithm was 0.927, which was better than similar prediction algorithms. Then, the empirical analysis of the proposed civil engineering budget course evaluation model showed that student satisfaction and student examination scores had increased to 92 and 94 points, respectively. The above results reveal that the proposed optimization algorithm and course evaluation model have good performance. Therefore, the implementation of the proposed curriculum evaluation method can significantly improve the learning efficiency of students and the teaching quality of civil engineering budgeting methods courses.