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Since the 21st century, with the continuous maturity of network technology and its integration with the education field, traditional face-to-face communication has gradually expanded to the virtual network environment. In the online learning environment, students can use the online platform to communicate directly with teachers, no longer limited by time and region. The time and space breakthrough of teacher-student interaction has brought development opportunities for teachers to constantly contact students with a long-term management mechanism. Based on this situation, this article uses artificial intelligence technology to build a network communication platform. This article first analyzes the application status of artificial intelligence technology in the network communication platform, and then introduces the artificial intelligence technology applied in this article. Then, this article uses artificial intelligence technology to design a network communication platform, and test the function and performance of the platform. The test results show that the function of the system is very accurate and reliable, and the performance of the system is sufficient to support nearly 10,000 users at the same time.

In order to study the standard energy consumption of air-conditioning system of subway vehicle and optimize the performance of air-conditioning system, it is necessary to clarify the air-conditioning representative operating conditions. In this paper, based on the tested data during real operation of subway vehicle in summer, the characteristics of the air-conditioning operating conditions are analyzed, and the construction method of the operating conditions is first proposed. Secondly, taking the tested line as an example, using the constructed operating conditions as sample data, the k-means clustering algorithm is used to identify the representative operating points of the air-conditioning in the cooling season. Finally, the energy consumption based on the representative operating points of the cooling season is calculated by LMS AMEsim software, the error between calculated results and field measurements data is within 10%, which verifies the accuracy of energy consumption calculation method based on these representative operating points.

In this study, the long-distance subway tunnel in Qingdao of China adopted semi-transverse extraction ventilation is taken as the research object. STAR-CCM+ software is used to simulate the smoke movement status and study the influence of different exhaust volumetric flow and heat release rates on the smoke layering length in tunnel with the semi-transverse extraction ventilation. Results show that the layering length has a linear decreasing relation with the natural logarithm of the exhaust volumetric flow under different heat release rates. There is opposite relation between the layering length and the natural logarithm of the heat release rate. Based on dimensional analysis and numerical simulations, the prediction model of the layering length and the critical exhaust volumetric flow in tunnel with semi-transverse extraction ventilation is proposed.

As a new generation of technology cloud computing has been widely applied to various fields, and owing to the problem of the security, the security problems of the data in the cloud computing platform has been becoming the focus of attention. Therefore, it is vital of cloud computing that to put forward a security mechanism to ensure the security of data in the environment of cloud computing. The related technology of Intrusion Detection is researched, and the thoughts of combining misuse detection and anomaly detection together are adopting. Then a method of anomaly detection based on data mining is given. It is the improved decision trees algorithm, and achieves a real-time updates of invading the signature database by misuse detection. It was improved through the experiment the application based on improved decision trees can detect the attack effectively and guarantee the security of the cloud computing.

Glow discharge plasma electrolysis (GDPE) of ethanol solutions for hydrogen generation was investigated in terms of discharged voltage, discharged polarity and ethanol concentration. H2 and acetaldehyde are the dominant products of ethanol decomposition during GDPE. Discharged polarity, discharged voltage and ethanol concentration have important influences on the energy consumption, concentration and output of hydrogen and acetaldehyde. The hydrogen yield (G(H2)) by cathodic GDPE is higher than that of anodic GDPE. The energy consumption (Wr) was 5.12 kJ L−1 when the applied voltage of cathodic GDPE was 1000 V. The hydrogen concentration of cathodic GDPE in gases keeps above 80%. The experiments indicate that GDPE of ethanol solutions is an effective technology producing hydrogen and acetaldehyde simultaneously with low CO2 emission.