## 1 Introduction  
In recent years, table-based question answering has emerged as a significant research direction in the field of Natural Language Processing (NLP), garnering widespread attention. As a common form of data representation, tabular data are extensively utilized in scientific research, business analysis, medical records, and financial reports, among other domains. Extracting knowledge from complex tabular data and enabling effective question-answering is becoming a challenging yet crucial task [1]. With the advancement of research, various table-based tasks and techniques have been progressively proposed, such as entity linking [2], schema enhancement [3], and table-based question answering [4, 5], among others. However, existing table-based question answering methods still face several limitations in practical applications. On the one hand, many methods rely heavily on extensive table pre-training [6; 7] and specialized model architectures designed specifically for tables [8]. On the other hand, these methods are often limited to handling specific types of tables and tasks, and they make strong assumptions about the structure of the tables and the types of tasks [7]. Therefore, improving the reasoning capabilities of models and enabling them to adapt to a wider variety of table types and tasks has become an urgent issue to address. Furthermore, Large Language Models (LLMs) also face the issue of \"hallucination\" when handling table-based question answering tasks, where the model may generate seemingly plausible but actually inaccurate answers, especially in the face of ambiguous contexts or knowledge gaps [9; 10]. This phenomenon not only affects the correctness of reasoning but also undermines the reliability of the models [11]. Although existing research has endeavored to mitigate this issue through knowledge updates and model fine-tuning [12; 13], merely adding random information does not effectively enhance the reasoning capabilities of the models. Instead, providing precise and contextually relevant external knowledge, particularly by augmenting the model's knowledge base through knowledge representation tools such as knowledge graphs, can significantly improve the accuracy of reasoning [14]. This study focuses on utilizing external knowledge to enhance the model's understanding of tabular data and reduce reasoning hallucinations, developing a structured reasoning approach to handle diverse tabular data and generate high-quality reasoning chains, and applying knowledge distillation for lightweight deployment while preserving reasoning performance in table-based QA tasks. To address these issues, this article proposes a multi-perspective enhancement method based on knowledge graphs and tree structures, aiming to improve the performance of models in table-based question answering tasks. First, by introducing knowledge graphs, we supplement the world knowledge not contained in the tables or the model, enhancing the model's understanding of the table content and reducing hallucination phenomena during reasoning. Second, considering the diversity of table data types, this article constructs tree-structured reasoning paths and utilizes confidence scores from each sub-branch for threshold filtering, generating high-quality reasoning chains from different perspectives for the same table data. This not only expands the base data volume but also enhances the model's multi-dimensional analysis capability for various types of table data. Finally, by combining the reasoning processes generated by the gpt-3.5-turbo model, we fine-tune the 7B model using knowledge distillation, achieving lightweight deployment of a general-purpose model while ensuring reasoning performance and reducing computational costs. In summary, the main contributions of this article are as follows: (1) We propose a multi-perspective enhancement method based on knowledge graphs and tree structures, which improves the model's reasoning capabilities and reduces hallucinations by incorporating external knowledge and structured reasoning paths.