Long non-coding RNAs (lncRNA) play important roles in many cellular life processes, and the subcellular localization of lncRNAs can bring key information for their functional identification. In response to the shortcomings of complex procedures, difficulty in replication, and high cost in identifying the subcellular localization of lncRNAs through traditional biochemical experimental methods, An attentional bi-directional long short-term memory (BiLSTM) and prototype network approach towards the prediction of lncRNA subcellular localization is proposed——BP-lncLoc. Firstly, the K-mer initial features are obtained from the original sequence data and balanced; Secondly, it combines the attention BiLSTM to effectively extract the deep implicit features of lncRNA sequences and optimize the neural network to deal with the gradient vanishing problem that may occur when dealing with high-dimensional data; thirdly, the prototype network prediction framework that does not rely on large-scale training samples is constructed for the small-sample nature of lncRNA subcellular localization data; finally, currently existing computational models lack interpretability, i.e., it is not known how the model makes decisions based on the input data, which is becoming more and more important with the rapid development of artificial intelligence and machine learning. In this paper, we achieve the interpretability of predictive models from the perspective of quantifying the importance of input features on output decisions. Compared with the state-of-the-art methods, achieves the best result of 98.89% accuracy on the public dataset, which provides a new idea for lncRNA subcellular localization prediction applications.