A series of effects caused by temperature change are the biggest problems faced by biological systems. These irregular environmental characteristics have brought new challenges to people and even animal groups. The immune function of human intestinal flora has a great protective effect on other organs and the body environment. It can help the human body carry out intestinal digestion, food absorption, nutritional metabolism, and so on. Based on the above situation, this paper uses the time series prediction model to study the factors affecting the imbalance of intestinal flora in the process of temperature change. Firstly, biological experiments are carried out with animals to simulate the human environment. Based on the sequence information of historical temperature change parameters, a temperature prediction device based on time series model is proposed. The effects of air factors and carbon dioxide content on the prediction results are evaluated by statistical analysis. Secondly, in order to ensure the accuracy of the experimental data, the neural network algorithm is used to optimize the model, and the white blood cell count is used to analyze the influence of temperature change on the intestinal flora structure of the two organisms. Finally, the experimental results are applied to the human environment to analyze the research results. The results showed that with the irregular change of temperature, the number of intestinal flora and internal colony structure also changed. The richness index in the normal temperature environment is relatively large, which can effectively explain the high richness of intestinal microflora in the experimental population. Further analyze the subjects and distinguish the animals according to sex. The number of Bacteroides in the intestinal flora of male animals was higher than that of female animals, but this phenomenon disappeared immediately after physical ligation. In addition, the intestinal flora abundance of female animals is higher than that of male animals, and the metabolic level is faster.

Entity relationship extraction is one of the key areas of information extraction and is an important research content in the field of natural language processing. Based on past research, this paper proposes a combined extraction model based on a multi-headed attention neural network. Based on the BERT training model architecture, this paper extracts textual entities and relations tasks. At the same time, it integrates the naming entity feature, the terminology labeling characteristics, and the training relationship. The multi-attention mechanism and improved neural structures are added to the model to enhance the characteristic extraction capacity of the model. By studying the parameters of the multi-head attention mechanism, it is shown that the optimal parameters of the multi-head attention are h = 8, dv = 16, and the classification effect of the model is the best at this time. After experimental analysis, comparing the traditional text entity relationship extraction model and the multi-head attention neural network joint extraction model, the model entity relationship extraction effect was evaluated from the aspects of comprehensive evaluation index F1, accuracy rate P, and system time consumed. Experiments show: First, in the accuracy indicator, Xception performance is best, reaching 87.7%, indicating that the model extraction feature effect is enhanced. Second, with the increase of the number of iterative times, the verification set curve and the training set curve have increased to 96% and 98%, respectively, and the model has a strong generalization ability. Third, the model completes the extraction of all data in the test set in 1005 ms, which is an acceptable speed. Therefore, the model test results in this article are good, with a strong practical value.

No abstract