This paper explores optimization strategies for self-supervised learning using unlabeled data. The authors propose a novel method that significantly improves the performance of self-supervised learning algorithms, achieving better accuracy and generalization. The method is validated across multiple datasets, demonstrating superior performance compared to traditional approaches. The study discusses how to optimize self-supervised learning strategies in the use of unlabeled data, introducing a new method for effectively utilizing unlabeled data for model training. Experimental results show significant performance improvements across various datasets, highlighting the method's robust generalization ability. Self-supervised learning uses the structure and intrinsic relationships within data to generate supervision signals, eliminating the need for manual annotations. The core idea is to learn model parameters by maximizing the likelihood function of the dataset. The authors propose combining deep reinforcement learning with generative adversarial networks to better utilize unlabeled data and enhance the generalization and robustness of self-supervised learning. Extensive experiments demonstrate that the method achieves excellent results across various datasets. Optimization strategies in self-supervised learning include designing effective self-supervised tasks, appropriate network structures, and loss functions. Contrastive learning is a key strategy that constructs sample pairs and compares their similarities to learn meaningful data representations. Loss functions such as contrastive loss and NCE loss are crucial for guiding model training and achieving better feature representations. The paper also discusses challenges in self-supervised learning, including data distribution imbalances and label noise. Future research directions include optimizing self-supervised learning models, proposing more effective task design methods, and combining self-supervised learning with other deep learning technologies like reinforcement learning and transfer learning. The study emphasizes the importance of interdisciplinary cooperation and continuous innovation to further promote the development and application of self-supervised learning in practical scenarios. The research provides valuable insights for advancing self-supervised learning technologies and has significant implications for related fields.This paper explores optimization strategies for self-supervised learning using unlabeled data. The authors propose a novel method that significantly improves the performance of self-supervised learning algorithms, achieving better accuracy and generalization. The method is validated across multiple datasets, demonstrating superior performance compared to traditional approaches. The study discusses how to optimize self-supervised learning strategies in the use of unlabeled data, introducing a new method for effectively utilizing unlabeled data for model training. Experimental results show significant performance improvements across various datasets, highlighting the method's robust generalization ability. Self-supervised learning uses the structure and intrinsic relationships within data to generate supervision signals, eliminating the need for manual annotations. The core idea is to learn model parameters by maximizing the likelihood function of the dataset. The authors propose combining deep reinforcement learning with generative adversarial networks to better utilize unlabeled data and enhance the generalization and robustness of self-supervised learning. Extensive experiments demonstrate that the method achieves excellent results across various datasets. Optimization strategies in self-supervised learning include designing effective self-supervised tasks, appropriate network structures, and loss functions. Contrastive learning is a key strategy that constructs sample pairs and compares their similarities to learn meaningful data representations. Loss functions such as contrastive loss and NCE loss are crucial for guiding model training and achieving better feature representations. The paper also discusses challenges in self-supervised learning, including data distribution imbalances and label noise. Future research directions include optimizing self-supervised learning models, proposing more effective task design methods, and combining self-supervised learning with other deep learning technologies like reinforcement learning and transfer learning. The study emphasizes the importance of interdisciplinary cooperation and continuous innovation to further promote the development and application of self-supervised learning in practical scenarios. The research provides valuable insights for advancing self-supervised learning technologies and has significant implications for related fields.