Text generation based on structured data is an important research direction in the field of natural language generation. It can transform structured data collected by sensors or statistically analyzed by computers into natural language texts suitable for human reading and understanding, thus becoming an important technology for automatic report generation. It is of great application value to study models of generating texts from structured data for the generation of analytical texts from various types of numerical data in reports. In this paper, we propose an encoder-decoder text generation model incorporating the coarse-to-fine aligner selection mechanism and the linked-based attention mechanism, which matches the characteristics of numerical data, and consider the problems of excessive content dispersion and failure to highlight descriptions in the process of generating analytical texts from numerical data. In addition, we also model the relationship between the domains to which the numerical data specifically belong in order to improve the correctness of the discourse order in generated texts. Experimental results show that the model proposed in this paper, which incorporates both mechanisms, has better performance in terms of metrics than the traditional model based on the content-based attention mechanism only, the model based on both the content-based attention mechanism and the linked-based attention mechanism, and the GPT2-based model. This proves the effectiveness of the proposed model in the task of generating analytical texts with numerical data.