Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a detailed demand analysis was carried out, and the overall structure optimization of the network was given; the business process and data flow of the main modules of the website (resource center module and collaborative learning module) were optimized. In order to further enhance the local search ability of the algorithm, a multiclass interactive optimization algorithm is proposed in combination with the Euclidean distance-based clustering method, which changes the teaching mode from “one-person teaching” to “multiperson teaching.” This clustering method has lower complexity and is beneficial to enhance the utilization of neighborhood information. At the same time, in order to enhance the diversity of the population and strengthen the connection between the subgroups, after the teaching phase, the worst students in each subgroup are allowed to learn from the best teachers of the population, and after the learning phase, individuals in a random subgroup are allowed to learn from other subgroups. The algorithm was tested in the experimental environment of unconstrained, constrained, and an engineering problem. From the test results, it can be seen that the algorithm is not easy to fall into the local optimum. Compared with other algorithms, the solution accuracy is higher and the stability is better. And it performed well in engineering optimization problems, thus verifying the effectiveness of the strategy.

Predicting students’ performance is very important in matters related to higher education as well as with regard to deep learning and its relationship to educational data. Prediction of students’ performance provides support in selecting courses and designing appropriate future study plans for students. In addition to predicting the performance of students, it helps teachers and managers to monitor students in order to provide support to them and to integrate the training programs to obtain the best results. One of the benefits of student’s prediction is that it reduces the official warning signs as well as expelling students because of their inefficiency. Prediction provides support to the students themselves through their choice of courses and study plans appropriate to their abilities. The proposed method used deep neural network in prediction by extracting informative data as a feature with corresponding weights. Multiple updated hidden layers are used to design neural network automatically; number of nodes and hidden layers controlled by feed forwarding and backpropagation data are produced by previous cases. The training mode is used to train the system with labeled data from dataset and the testing mode is used for evaluating the system. Mean absolute error (MAE) and root mean squared error (RMSE) with accuracy used for evolution of the proposed method. The proposed system has proven its worth in terms of efficiency through the achieved results in MAE (0.593) and RMSE (0.785) to get the best prediction.

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This paper uses the data envelopment analysis method to construct an emerging resource allocation optimization algorithm to deeply analyze and study preschool education resource allocation. This paper combines information related to distributed systems and database application practices, and HBase, a distributed data storage system, is selected as the research object. The relationship between hardware configuration parameters and the throughput and response latency of HBase is modeled using the random forest algorithm, an improved particle swarm algorithm is designed and implemented to optimize the mathematical model of the relationship between resource allocation and capital cost, and the optimization results are verified in a real environment to realize the optimization of HBase resource allocation scheme. In this paper, duplicate solution sets are removed based on the objective function in the resource allocation model. Finally, NSGA-II is compared with SPEA2, MOEA/D, and IBEA in this model for simulation experiments to verify the efficient solution of NSGA-II to the constructed model through subjectivity presentation as well as objective performance analysis and to provide a decision solution with a theoretical support for resource allocation optimization. This study provides countermeasure suggestions for each kindergarten to improve the efficiency of resource management, optimize the scale of operation, and increase technological progress, which is based on the findings of the empirical analysis and field interviews, and thus can effectively help kindergartens to improve the efficiency of resource allocation.

The progress in computer sciences and information technology provides more teaching modes. Recently, the microlecture has drawn extensive attentions, which could make the knowledge points clear in a shorter teaching time than traditional ways. In addition, it is not limited by time and space. This study makes some explanations and discussions on microlecture in order to apply it to higher normal education. First, the basic concept of microlecture is presented to explain the background. Afterwards, the teaching mode and advantages of microlecture are discussed. Accordingly, its potential in higher normal education can be inferred. Finally, some feasible ways are suggested to apply microlecture to the higher normal education. Conclusions are drawn based on all these analyses. And it is promising that microlecure could make contributions to the teaching reform of higher normal education.