To address the degradation of WiFi fingerprinting accuracy caused by multipath effects and signal obstruction in complex indoor environments, this paper proposes an RSSI-based localization algorithm integrating a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM). The method leverages the spatial feature extraction capability of CNN and the temporal sequence modeling strength of BiLSTM to achieve deep spatiotemporal feature fusion. MATLAB simulation experiments compare the proposed model with CNN, CNN-LSTM, LR, KNN, RF, GBDT and SVM. Results show that the proposed CNN-BiLSTM achieves the best performance, reducing average, maximum and standard deviation of positioning errors by 52.1%, 53.2% and 55.6%, respectively. Monte Carlo trajectory experiments further demonstrate the superior accuracy and robustness of CNN-BiLSTM in continuous localization. The proposed model effectively suppresses RSSI fluctuations and provides a feasible solution for high-precision indoor WiFi fingerprinting.