The continuous evolution of Android malware threatens device security, while existing visualization-based schemes often rely on single or random views, limiting their ability to capture malicious behavior comprehensively. To address this limitation, we propose CansDroid, a multi-view detection scheme integrating three complementary views: API sensitivity, operational context, and runtime environment. Each view is processed with dedicated filtering strategies and deep learning techniques. We introduce a Mixture of Experts (MoE) model that enables early-stage multi-view feature collaboration, regularized by balance loss and expert diversity enhancement loss to ensure balanced expert utilization and representation diversity. Experimental results show that CansDroid achieves a detection accuracy of 99.38%, outperforming state-of-the-art schemes, and successfully identifies 674 previously unseen malware samples, demonstrating its effectiveness in detecting evolving Android threats.