The emergence and development of high dimensional big data streams have presented a great challenge to the traditional machine learning and data mining algorithms. Based on the characteristics of data flow, first we construct an adaptive incremental feature extraction algorithm model. Then, according to the environment with noise, we establish an incremental manifold learning algorithm model based on feature space alignment to solve the small size sample problem. Finally, the regularization optimization framework of manifold learning is constructed to solve the problem of dimensionality reduction errors of high-dimensional data flow in feature extraction process, and then the optimal solutions are obtained. Experimental results show that the proposed algorithm framework conforms to the three evaluation criterions of manifold learning algorithm: Stability, enhancement, and the learning curve can rapidly increase to a relative stable level. Thus the efficient learning of high-dimensional data streams can be realized.