The real-time， unboundedness， and dynamically changing characteristics of data streams lead to time-varying data distributions， a phenomenon termed concept drift. Traditional methods for detecting and adapting to concept drift typically rely on the assumption of complete label availability. However， the prohibitively high cost of data annotation in real-world scenarios makes fully supervised learning approaches infeasible. Consequently， active learning is commonly utilized for classification tasks with scarce labels. Nevertheless， in streaming environments， factors such as concept drift and single-label strategies often introduce sampling bias into active learning. To address these challenges， a Multiple Active Learning method based on Concept Drift detection （MALCD） was proposed. An online deep neural network model incorporating dynamically weighted skip connections was designed and combined with a weakly supervised drift detection method to detect concept drift. At the same time， multiple sampling strategies were incorporated to apply differentiated processing strategies across different sample regions. By integrating multiple active learning methods with concept drift detection techniques， this method can precisely select data with high uncertainty and categorical diversity while efficiently avoiding redundancy. Experimental results on eight real-world and synthetic datasets demonstrate that MALCD achieved the highest average ranking in cumulative accuracy compared to Online Ensemble Adaptive Classification （AC_OE） method， Weakly Supervised Concept Drift Detection （WSCDD） method， etc. This indicates that the MALCD can quickly learn new concept distributions after drift occurs， thereby enhancing the model's overall generalization performance.
