Entity-relationship extraction task is one of the very important research directions in the field of natural language processing, aiming at identifying and determining the existence of specific relationships between entity pairs from unstructured text. The study firstly introduces the related theories of graph neural networks in terms of graph representation learning and graph neural networks, and then makes full use of the information of dependent syntactic trees to propose a relationship extraction model based on dependency graph convolution (DGGCN). The validity of the model and the entity extraction effect are verified through relevant experiments.The DGGCN model is fully experimented on the public datasets NYT and WebNLG, and the F1 value is effectively improved.According to the results of the ablation experiments, it is shown that the DGGCN model improves the entity and ternary extraction results by 0.5% and 4.3%, respectively. In the long and short distance entity extraction results, the DGGCN model outperforms the benchmark model in both long and short distance entity relations, but the extraction performance gap between short and long distance entity relations is still large and needs further improvement.

Urban safety development is one of the guarantees for the overall development of the city, and the study uses Delphi method, entropy weight method and TOPSIS method in the assessment of urban safety development. An improved Delphi-entropy weight-TOPSIS combination assessment model is constructed to evaluate the urban safety development. The evaluation index system of urban safety development is constructed, and the evaluation indexes of urban safety development are calculated by Delphi method and entropy weight method respectively, and the subjective and objective weights of the evaluation indexes of urban safety development are derived, and finally, the comprehensive weights are calculated by the method of combined weight assignment. The comprehensive weights of the guideline layer of the urban safety development evaluation index system are 0.1874, 0.2080, 0.2005, 0.2187, and 0.1854, respectively.The evaluation index system is used for empirical research, and City A is taken as the object of the research to assess its urban safety development status during the 10-year period from 2014 to 2023. From the evaluation results, it is known that the overall urban safety development of City A during the 10-year period shows an upward trend, with slight fluctuations in the process, but the overall development is good, and the evaluation score of urban safety development improves from 0.4657 points in 2014 to 0.6479 points in 2023.

As an important part of economic activities, logistics industry ushers in new development opportunities and challenges in the wave of digital transformation. The study explores the path of integration and development of digital economy industry and logistics industry, designs the path of building intelligent logistics ecosystem, and constructs the logistics distribution path optimization model based on time window. When analyzing and solving the logistics distribution path optimization problem, the ant colony algorithm (ACO) is improved by introducing the hierarchical idea of the artificial bee colony algorithm (ABC) and limiting the pheromone concentration on each path, controlling it within a known range, to make up for the shortcomings of the ant colony algorithm of precocious maturity and search stagnation. Using MATLAB software to simulate the logistics and distribution of M fresh food e-commerce enterprises, the comprehensive cost solved based on ABC-ACO algorithm is 75.64 yuan and 33.45 yuan less than the results of ACO and GA solving, respectively, and the optimal route traveling mileage is 21.35 km and 6.03 km shorter than the mileage solved by ACO and GA solving, respectively. It shows that the performance of the improved ant colony algorithm is better than that of the basic ant colony algorithm and the genetic algorithm, and it points out the direction for the future logistics and distribution of the distribution center. The empirical analysis found that the digital economy industry and logistics industry show a synergistic trend, and there is a large space for integration and development.

The concept of “Internet+Sports” has promoted the application of artificial intelligence and other emerging technologies in the field of sports. This paper mainly focuses on the special physical training, and explores the application and realization path of artificial intelligence technology in physical training test. In this paper, PSO-BP model is constructed based on BP neural network optimized by PSO intelligent algorithm and applied in physical training test. In addition, for the classification of physical training, this paper follows the basic principles of physical training system construction, establishes the physical training measurement index system through the results of expert solicitation, and determines the weights of each index by using the hierarchical analysis method. Through the empirical analysis of the PSO-BP model in this paper, it can be seen that the fitting results of the training samples of male and female students show that the corresponding correlation coefficients of male and female students are 0.99908 and 0.99898, respectively.The errors of the evaluation output values of the physical training measurements and the expected values are within ±3.5, and the prediction error of the BP neural network model optimized by the PSO algorithm is significantly reduced, and the relative errors of the evaluation of male and female students are reduced by 0.988% and 0.833%, respectively. The results show that the results of physical training measurement and evaluation using PSO-BP neural network model are more accurate, which proves that the performance of PSO-BP neural network in this paper has been effectively improved and optimized, and at the same time, it can meet the application requirements of physical training measurement and evaluation.

The extreme high temperature and erosive environment service environment in bridge construction puts forward higher requirements for high performance concrete and other aspects of performance. In this paper, compound mineral admixture is selected as a research breakthrough, and X-ray diffraction analysis (XRD) and Raman spectroscopy are used to explore the micromechanical behavior of compound mineral admixture in high-performance concrete. In the Raman spectral analysis, the stress distribution of the fitted curve of the compound mineral admixture is more flat and uniform, and the offset of the G’ peak position is higher than that of the reference concrete and the single-mineral-admixture concrete, and the stress can reach 2.5 MPa under 1% strain, showing good interfacial bond, stress transfer efficiency, etc. The physical phase data of the XRD also shows the frost resistance of compound mineral admixture, with the ability to mitigate carbon dioxide, and the ability to reduce the carbon footprint of the concrete, with the ability to reduce the carbon dioxide. The XRD data also show the frost resistance of the compound mineral admixture, which has the performance of slowing down carbonization. The NSGA-II algorithm is introduced and improved to propose a concrete proportion optimization model. The final evaluation function converges from 35 generations and the final value is 0.4558, which achieves the adaptive optimization of compound mineral admixture.

Semantics in public English texts are more challenging to understand accurately because they are influenced by specific contextual contexts. Traditional English text semantic understanding methods do not design their semantic understanding methods based on the conceptual semantic features of the text, and they have the problem of poor accuracy in understanding the deep semantics of English texts. For this reason, the article takes the public English text semantic algorithm as the research perspective, firstly conducts relevant theoretical research on English text semantic feature representation, then explores the text semantic extraction method based on the Dependency Tree-CRF, and deepens the understanding of English text semantics through the conceptualization and attention embedding methods. In the experiment of comparing the semantic coherence model with manual scoring, the experiment shows that by applying the semantic analysis model designed in this paper to the task of correcting the English writing of domestic college students and comparing it with the experimental results of manual scoring, it is found that the average absolute error between the scoring of the English compositions by this paper’s model and the scores of the compositions corrected by the teachers is 3.2051, i.e., the difference between the results of the manual correcting and the results of the correction by this paper’s model is It is not big, from which we can get that the model of this paper has good practical value.