In recent years, spectral clustering has become a well-known and effective algorithm in machine learning. However, traditional spectral clustering algorithms are designed for single-view data and fixed task setting. This can become a limitation when dealing with new tasks in a sequence, as it requires accessing previously learned tasks. Hence it leads to high storage consumption, especially for multi-view datasets. In this paper, we address this limitation by introducing a lifelong multi-view clustering framework. Our approach uses view-specific knowledge libraries to capture intra-view knowledge across different tasks. Specifically, we propose two types of libraries: an orthogonal basis library that stores cluster centers in consecutive tasks, and a feature embedding library that embeds feature relations shared among correlated tasks. When a new clustering task is coming, the knowledge is iteratively transferred from libraries to encode the new task, and knowledge libraries are updated according to the online update formulation. Meanwhile, basis libraries of different views are further fused into a consensus library with adaptive weights. Experimental results show that our proposed method outperforms other competitive clustering methods on multi-view datasets by a large margin.