Concept drift poses a challenge in the field of data stream mining. Most existing online deep learning methods rely on a single objective, failing to sufficiently capture the latent feature information embedded in the data, which hinders rapid adaptation to distribution changes. To address these issues, this paper proposes a Multi-objective Joint Optimization of Deep Network (MJOD) model. Specifically, the feature connection network (FCN) connects features from different layers via dense feature units, constructing an integrated classification objective to fully utilize both shallow and deep information for precise prediction. Subsequently, the feature refinement network (FRN) compares historical representative information with current data to build a compressed reconstruction objective, thereby capturing changes in data distribution. Furthermore, the feature enhancement network (FEN) extracts spatial structure information from the data and constructs an information balance objective to retain the information most relevant to the task. By jointly optimizing the integrated classification, compressed reconstruction, and information balance objectives, the model maximizes the utilization of feature information within the streaming data, enabling rapid learning of new distributions. Experimental results demonstrate that MJOD is superior to other baseline methods across various drift scenarios.