In recent years, data aggregation technology has become a common approach to addressing the imbalance between data collection and user privacy in IoT communication. Building on traditional data aggregation, multi-subset data aggregation enables the extraction of distributional characteristics of the data, rather than merely obtaining the total sum. However, most existing multi-subset aggregation schemes rely on single-message encryption algorithms, which can only encrypt a single message at a time, making it impossible to compute data variance alongside the mean, thereby restricting the analysis of data perturbation. To address this issue, we propose a multi-subset data aggregation scheme with enhanced statistical analysis capabilities. This scheme is designed based on an improved BGN encryption algorithm, enabling the computation of sums, variances, and population distributions across different numerical ranges. It also incorporates BLS short signatures to ensure the confidentiality and integrity of user data during transmission, supports batch verification, and functions independently of third-party institutions. Our scheme is characterized by strong fault tolerance and robustness, allowing for dynamic user participation. Experimental analysis shows that, compared to existing multi-subset aggregation schemes, our approach achieves superior statistical analysis capabilities with lower computational overhead.