Aiming at the problem that Artificial Neural Network (ANN) has limited memory capability for knowledge reasoning oriented to Knowledge Graph (KG) and the KG cannot deal with uncertain knowledge, a reasoning method named DNC-BN was propsed based on Differentiable Neural Computer (DNC) and Bayesian Network. Firstly, using Long Short-Term Memory (LSTM) network as the controller, the output vector and the interface vector of network were obtained by processing the input vector and the read vector obtained from the memory at each moment. Then, the read and write heads were used to realize the interaction between the controller with the memory, the read weights were used to calculate the weighted average of data to obtain the read vector, and the write operation was performed by combining the erase vector and write vector with the write weights, so as to modify the memory matrix. Finally, based on the probabilistic inference mechanism, the BN was used to judge the inference relationship between the nodes, and the KG was completed. In the experiments, on the WN18RR dataset, DNC-BN has the Mean Rank of 2 615 and the Hits@10 of 0.528; on the FB15k-237 dataset, DNC-BN has the Mean Rank of 202, and the Hits@10 of 0.519. Experimental results show that the proposed method has good application effect on knowledge reasoning oriented to KG.

Aiming at the problems that simple Recurrent Neural Network (RNN) cannot memorize information for a long time and single Convolutional Neural Network (CNN) lacks the ability to capture the semantics of text context, in order to improve the accuracy of text classification, a sentiment analysis model G-Caps (Gated Recurrent Unit-Capsule) was proposed, which combines Gated Recurrent Unit (GRU) and capsule features. First, the contextual global features of the text were captured through GRU in order to obtain the global scalar information. Second, the captured information was iterated through the dynamic routing algorithm at the initial capsule layer to obtain the vectorized feature information representing the overall attributes of the text. Finally, the features were combined in the main capsule part to obtain more accurate text attributes, and the sentiment polarity of the text was analyzed according to the intensity of each feature. Experimental results on the benchmark dataset MR (Movie Reviews) showed that compared with the CNN + INI (Convolutional Neural Network + Initializing convolutional filters) and CL_CNN (Critic Learning_Convolutional Neural Network) methods, G-Caps had the classification accuracy increased by 3.1 percentage points and 0.5 percentage points respectively. It can be seen that the G-Caps model effectively improves the accuracy of text sentiment analysis in practice.