Exploration of Optimization Paths for Retrieval Algorithms Based on Deep Learning Models

In the era of big data, the rapid and accurate retrieval of massive information is of great significance. Traditional retrieval algorithms struggle to meet the complex and diverse retrieval requirements. This paper focuses on exploring the optimization paths for retrieval algorithms based on deep learning models, aiming to enhance the performance and efficiency of retrieval algorithms. By combing the basic theories of deep learning and retrieval algorithms, this paper deeply analyzes common retrieval algorithms based on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers, clarifying their advantages and limitations. In response to the challenges at the data, algorithm, and application scenario levels, optimization paths such as data preprocessing and augmentation, model structure improvement and hyperparameter tuning, and multimodal fusion retrieval are proposed, and verified through practical cases in the fields of image retrieval and text retrieval. The research shows that the optimized retrieval algorithms have significantly improved in key indicators such as accuracy and recall rate, providing a useful reference for the further application of deep learning in the retrieval field.