Objective The feature space of feature-evolving data streams dynamically changes over time. Traditional incremental learning methods are constrained by the assumption of a fixed feature space and cannot be directly applied to the learning scenario of feature-evolving data streams. Therefore to address the problems of classification models mismatching with current data features and model prediction performance being affected by noise interference when mining feature-evolving data streams an incremental learning method tailored to feature-evolving data streams was proposed. Methods Firstly by introducing fuzzy membership functions and combining them with an incremental twin support vector machine model classifiers were robustly trained and updated. When new features appeared new classifiers were retrained and the mapping relationship between new and old features was fitted using a local linear weighted regression algorithm. Thus when old features disappeared the trained old classifiers were projected into the new feature space for continued updating using the learned mapping relationship. Finally two different ensemble strategies were combined to merge the new and old classifiers for joint prediction. Results Through extensive simulation experiments the proposed method improved classification accuracy by 0. 3% to 21. 7% compared with baseline methods. On datasets with different signal-to-noise ratios the overall performance of the classification model was superior to that of the baseline model and the model ?? s classification effectiveness was less affected by artificially increasing the noise ratio. Conclusion The proposed method is verified to construct an efficient and stable classification model which not only enhances model prediction accuracy but also reduces the interference of noise on classification performance thus strengthening the adaptive learning capability of the model for feature-evolving data streams.