A big data-based answer position acquisition method, relating to the field of artificial intelligence. Said method comprises: obtaining text information and question information to be processed, separately performing vectorization on each word in the text information and the question information, and obtaining word vectors corresponding to the text information and word vectors corresponding to the question information (S101); performing feature extraction on the word vectors corresponding to the question information by means of a bidirectional recurrent neural network Bi-LSTM, compressing an extracted multi-dimensional feature matrix to a preset dimension, and obtaining question encoding information (S102); adding a position vector and the question encoding information to each word vector of the text information, and obtaining text encoding information corresponding to the text information (S103); performing feature extraction on the text encoding information by means of multiple preset convolutional layers, and obtaining text feature information corresponding to the text information (S104); performing sequence tagging on the text feature information by means of a bidirectional recurrent neural network Bi-LSTM, and obtaining a first probability and a second probability of each word in the text information (S105); obtaining a word corresponding to a largest first probability value to serve as an answer starting position, and a word corresponding to a largest second probability value to serve as an answer ending position (S106). The present invention implements answer predictive analytics and solves the problem where current question and answer models have a large amount of parameters and a long time is expended on algorithm training.
BIG DATA-BASED ANSWER POSITION ACQUISITION METHOD, APPARATUS, DEVICE, AND MEDIUM