The current robotics field, led by a new generation of information technology, is moving into a new stage of human-machine collaborative operation. Unlike traditional robots that need to use isolation rails to maintain a certain safety distance from people, the new generation of human-machine collaboration systems can work side by side with humans without spatial obstruction, giving full play to the expertise of people and machines through an intelligent assignment of operational tasks and improving work patterns to achieve increased efficiency. The robot’s efficient and accurate recognition of human movements has become a key factor in measuring robot performance. Usually, the data for action recognition is video data, and video data is time-series data. Time series describe the response results of a certain system at different times. Therefore, the study of time series can be used to recognize the structural characteristics of the system and reveal its operation law. As a result, this paper proposes a time series-based action recognition model with multimodal information fusion and applies it to a robot to realize friendly human-robot interaction. Multifeatures can characterize data information comprehensively, and in this study, the spatial flow and motion flow features of the dataset are extracted separately, and each feature is input into a bidirectional long and short-term memory network (BiLSTM). A confidence fusion method was used to obtain the final action recognition results. Experiment results on the publicly available datasets NTU-RGB + D and MSR Action 3D show that the method proposed in this paper can improve action recognition accuracy.

In recent years, with the continuous development and progress of information technology and science and technology, big data has entered all walks of life, integrated into the lives of the public, and has become a necessity for social operation; the gradual development of artificial intelligence has also made life in modern times. People in society are more and more convenient. However, the development of science and technology is also accompanied by corresponding problems, and the war in information has gradually started. This paper simulates the possible information security through the hidden Markov model and then verifies the feasibility and effectiveness of the situation assessment method and the situation prediction method, in order to effectively evaluate the relevant information security level and effectively predict the accuracy of the situation value. The experimental results show that the fluctuation of the situation value corresponds to the different attack behaviors carried out by the attacker, accurately describes the information security status of the system, and verifies the effectiveness and accuracy of the situational awareness method proposed in this paper, while the situation prediction method based on ARIMA predictable short-term changes in situational values can be used for short-term forecasts that require high accuracy.

For the cross-linguistic similarity problem, a twin network model with ordered neuron long- and short-term memory neural network as a subnet is proposed. The model fuses bilingual word embeddings and encodes the representation of input sequences by ordered neuron long- and short-term memory neural networks. Based on this, the distributed semantic vector representation of the sentences is jointly constructed by using the global modelling capability of the fully connected network for higher-order semantic extraction. The final output part is the similarity of the bilingual sentences and is optimized by optimizing the parameters of each layer in the framework. Multiple experiments on the dataset show that the model achieves 81.05% accuracy, which effectively improves the accuracy of text similarity and converges faster and improves the semantic text analysis to some extent.