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In order to solve the problems of low security and response efficiency and slow running speed of the current designed higher education system, a higher education system based on artificial intelligence technology is designed. Firstly, according to the characteristics of artificial intelligence technology, intelligent teaching system, agent technology, and data mining technology are introduced in detail. Then it analyzes the overall and detailed functional requirements of the system and adaptively generates knowledge content and teaching mode suitable for students’ ability and personality by using intelligent reasoning ability and the collection and analysis of students’ personality characteristics. Through data mining of intelligent teaching system, the decision tree about curriculum is obtained, and the students’ cognitive ability is calculated. Based on the theory of cognitive science, using the “double master” teaching mode, combined with agent technology and intelligent teaching system, the system function is divided into six modules. Through the design of database structure and data table, the design of higher education system based on artificial intelligence technology is realized. The experimental results show that the proposed method has high security and response efficiency, fast running speed, and good teaching effect.

The personalized recommendation method of higher education resources currently cannot carry out multidimensional association analysis of learners, situations, and resources and cannot extract accurate resources for learners, resulting in a large error. This study constructs a personalized recommendation method for higher education resources based on multidimensional association rules. This algorithm clarifies the multidimensional association rules, extracts the key data from massive educational resources, and groups the same kind of data by using a frequent itemset algorithm for mining association rules, namely, the Apriori algorithm. Combined with traditional data mining technology, this study constructs a new personalized recommendation model for education resources based on multidimensional association rules, which achieves the accurate extraction of higher education resources and ensures the matching degree between learners and resources. The experimental results show that the personalized recommendation model of educational resources in this study effectively makes up for the disadvantages of the traditional data mining algorithms, with a small root mean square error and short data mining time, within 20 ms.

Process parameters of rotating velocity, welding speed, Zn interlayer thickness, and ultrasound power are optimized by the hybrid of back propagation neural network (BPNN) and gray wolf optimization algorithm (GWOA) to obtain a high‐quality Zn‐added ultrasound‐assisted friction stir lap welding joint of 7075‐T6 Al/AZ31B Mg dissimilar alloys. The results state that the prediction accuracy of the trained BPNN model is acceptable. The optimal process parameters combination is obtained by the GWOA which is combined with the trained BPNN. The verification tests are performed under the executable optimal solution, which consists of the rotating velocity of 1054 rpm, the welding speed of 54 mm min−1, the Zn interlayer thickness of 0.05 mm, and the ultrasound power of 1568 W. The tensile shear load of the joint reaches 9.05 kN, and the strength is 11.8% larger than that of the reported optimal joint. The artificial intelligence optimization method of GWOA combined with BPNN can accurately predict and optimize the joint strength, which has great time and economic advantages.

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