Naive Bayes (NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independence assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these methods, due to its high efficiency and easy implementation, the filter-attribute-weighted NB methods have received great attentions. However, there still exist several challenges, such as the poor representation ability for a single index and the fusion problem of two indexes. To overcome the above challenges, we propose a general framework of an adaptive two-index fusion attribute-weighted NB (ATFNB). Two types of data description category are used to represent the correlation between classes and attributes, the intercorrelation between attributes and attributes, respectively. ATFNB can select any one index from each category. Then, we introduce a regulatory factor $\beta$ to fuse two indexes, which can adaptively adjust the optimal ratio of any two indexes on various datasets. Furthermore, a range query method is proposed to infer the optimal interval of regulatory factor $\beta$. Finally, the weight of each attribute is calculated using the optimal value $\beta$ and is integrated into an NB classifier to improve the accuracy. The experimental results on 50 benchmark datasets and a Flavia dataset show that ATFNB outperforms the basic NB and state-of-the-art filter-weighted NB models. In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive regulatory factor $\beta$. Auxiliary experimental results demonstrate the improved model significantly increases the accuracy compared to the original model without the adaptive regulatory factor $\beta$.