As intelligent vision tasks become more widespread, enhancing image quality before further computational analysis is crucial. Recently, deep learning has shown potential for automated pre-computing enhancement, but it typically requires substantial computational resources and is hard to adapt to in multiple situations without re-training. In practice, image enhancement often demands flexible adjustments based on different situations and subsequent computation devices, such as optical computing. Therefore, we propose SAEnhancer, a situation-adaptive neural network for fast pre-computing image enhancement. It learnsfrom a small sample set to achieve personalized and adaptive enhancements for various situations without re-training, using semantic-aware embedding for precise color adjustments, surpassing traditional 3D lookup tables (LUTs), and enhancing computational effectiveness in intelligent vision applications.