In practice, the number of views can increase over time, and repeatedly fusing all views upon the arrival of each new view can result in high computational costs and accumulated redundancy. Additionally, early-acquired views may become unavailable due to privacy, storage, or data expiration issues, leading to reduced consistency and poor clustering performance. To solve these issues, we propose Balanced Learning for Incremental Multi-View Clustering (BIMC), which incrementally constructs a unified matrix to preserve view information over time. To further enhance clustering performance, each new view is integrated using balanced learning that reduces feature distribution shifts and erroneous connections between clusters while maintaining consistency within clusters. Finally, to further enhance consistency, cluster labels are directly obtained from the consensus graph by enforcing a Laplacian rank constraint, enabling unified graph construction and clustering. Experimental results demonstrate that BIMC achieves superior clustering performance and efficient view fusion across diverse multi-view datasets.