Indoor navigation systems are gaining traction due to their resistance to electromagnetic interference, abundant spectrum resources, and energy efficiency, underscoring the importance of indoor visible light positioning technology. Recent research focuses on using deep learning to enhance positioning accuracy, yet challenges remain in training costs, model efficiency, and performance in low Signal-to-Noise Ratio (SNR) scenarios. To address these issues, we propose a novel Long Short Term Memory network-Convolution Residual Network (LSTM-CRN) algorithm with a dataset construction method based on pilot extraction. Additionally, we introduce the Kolmogorov-Arnold Network (KAN) to improve accuracy under low SNR conditions. Extensive simulation results show that the network model trained on the dataset constructed by the pilot extraction method has higher localization efficiency and accuracy, especially compared with the network model trained directly using the received data to construct the dataset. The LSTM-KAN algorithm is trained on the dataset constructed by our method in this paper, and its average localization accuracy is verified to be 3.8 cm (SNR = 30). It also shows better localization accuracy, efficiency, and real-time performance than existing mainstream methods under different SNR conditions, proving that this method is the state-of-the-art in the system described in this article.