The adaptability of deep neural network (DNN) models in non-stationary scenarios remains a significant challenge in the field of artificial intelligence. In particular, when concept drift occurs, models with predefined architectural parameters struggle to adapt to evolving data distributions. Existing depth adjustment methods for DNNs do not adequately evaluate the validity of depth expansion and overlook the synergy between network weights and depth during the adjustment process. To address this issue, this study proposes an adaptive online deep neural network (AODNN). AODNN assesses the effectiveness of depth expansion by jointly analyzing the network’s loss trends, classifier weights, and changes in mutual information, thus enabling adaptive depth growth. In addition, it employs parameter update optimization to select intermediate classifiers for participation in backpropagation, reducing interference from deeper intermediate classifiers to shallower ones and accelerating convergence. Comparative experiments are conducted between AODNN and state-of-the-art methods on multiple real-world and synthetic datasets containing concept drift. The experimental results validate the effectiveness of AODNN’s adaptive depth growth and parameter update optimization strategies, demonstrating its capability to effectively capture changes in data distribution and mitigate the impact of concept drift. Across key performance metrics, AODNN demonstrates significant advantages, notably outperforming HBP, ANSN, and EODL in cumulative accuracy, surpassing ATNN, ANSN, and EODL in F1-score, and achieving significantly better MCC compared to ANSN and EODL. Furthermore, AODNN surpasses current state-of-the-art methods in aspects of model convergence speed.