<p>Multi-source interval-incomplete data are widely encountered in real-world applications, such as medical testing, climate monitoring, remote sensing, and economic analysis. However, some of these data sources may have relatively low importance, or even no practical value. Consequently, how to effectively perform information fusion and attribute reduction on multi-source data remains a critical challenge. This paper proposes an adaptive swarm intelligence attribute selection method for a multi-source incomplete interval-value data based on conditional information amount and mutual information. First, the metric formulas on single-source incomplete interval-valued data are established, and the neighborhood granularity structure with respect to an adjustable parameter is constructed accordingly to the defined metric. Then, a fusion method based on the minimizing of conditional information amount is presented to fuse a multi-source incomplete interval-value data into a single-source incomplete interval-valued data. This method is able to select important and reliable information sources. To identify the most effective subset of features, two adaptive strategies are incorporated into the standard whale optimization algorithm (WOA) to improve its parameter selection process. Without modifying the original search operators, an adaptive WOA-based attribute selection method is developed by leveraging mutual information. The proposed method focuses on improving the robustness and effectiveness of the attribute selection process. Finally, comprehensive experiments are conducted on 12 benchmark datasets to evaluate the effectiveness of the proposed method. The results show that the proposed information fusion method has certain advantages in terms of approximate classification accuracy and quality, while the designed attribute selection algorithm surpasses several state-of-the-art methods in classification accuracy, with statistical analyses further confirming its advantage.</p>