Aiming at the ranging error problem of ultra-wideband in actual environment, a UWB ranging error mitigation algorithm based on improved sparrow search algorithm and convolutional neural network bi-directional long short-term memory attention model is proposed. Tent mapping is adopted, adaptive adjustment method is used, combined with northern goshawk algorithm, and spiral flight strategy is adopted in the following stage to improve the SSA algorithm, improve the global search performance of the algorithm and avoid falling into the local optimum. The BiLSTM model and attention mechanism are used to improve the CNN-LSTM model, and the CNN-BiLSTM-Attention model is constructed to improve the model′s ability to process long sequence data, so that the model has more accurate weight distribution for data. TANSSSA is used to optimize the hyperparameters of the CNN-BiLSTM-Attention model, and the TANSSSA-CNN-BiLSTM-Attention model is constructed. In the model performance verification experiment, the average absolute error of SSA-CNN-BiLSTM-Attention, CNN-BiLSTM-Attention, CNN-BiLSTM, CNN-LSTM-Attention, CNN-LSTM, GRU and TCN models was reduced by 12.05%~62.31%. In the actual environment, the average absolute error of TANSSSA-CNN-BiLSTM-Attention was reduced by 45.70%~83.82% compared with the other seven models, and the ranging error was effectively alleviated.