Application Mathematics Universal Method Based on UniLM and Word Vector Adjustment Algorithm

This study proposes a universal solution based on UniLM (Unified Language Model) and word vector adjustment algorithm to address the high computational complexity and poor model generalization ability of traditional methods in solving applied mathematical problems. Firstly, utilizing UniLM’s multitasking capabilities to handle complex language structures enhances the model’s understanding of mathematical problems. Next, the word vector adjustment algorithm is introduced to dynamically reinforce mathematical terms, in order to improve the model’s ability to accurately capture key mathematical concepts. In addition, the data has been standardized, including tagging and feature extraction, to ensure that the model can efficiently process input data. The experimental results show that the accuracy of this method is significantly improved on different types of mathematical problems, with accuracy rates of $\mathbf{9 5. 2 \%}$ and $\mathbf{9 0. 5 \%}$ for addition and subtraction problems, respectively. At the same time, it has high computational efficiency and generalization ability, indicating that the proposed method has obvious advantages in solving mathematical problems and is expected to provide new solutions and practical references for this field.