Data mining technology can analyze and mine university management data, provide more data support for university management, and play an important role in optimizing teaching quality, but these technologies are rarely applied to the work of university counselors. Based on this, this paper studies the construction and research of college counselors’ innovative work mode based on data mining technology. Based on the simple analysis of the impact of big data on the traditional working mode of counselors, this paper introduces the application of data mining commonly used in colleges and universities and puts forward the existing shortcomings. The innovative working mode of college counselors is designed. In the analysis of students’ daily behavior, the advantages of cluster analysis and support vector machine are used to analyze students’ consumption behavior. The Apriori algorithm is applied to the student achievement early warning management to improve the Apriori algorithm. Simulation results show that the proposed algorithm can shorten the running time, reduce the number of frequent item sets, and improve the classification accuracy.

In the process of civic and political education, counselors should not only provide good service guidance for students’ learning but also deeply understand students’ ideological dynamics and psychological conditions. This would help to guide them in establishing healthy ideological concepts and moral qualities. The continuous development of ideological education cannot be achieved without the assistance of an experienced counselor team. There is a nationwide requirement to strengthen the professionalism of college counselors which is presently lagging. There exists a gap between the demand of the job seekers and the relevant products who fail to meet the need of the college administrators, working counselors, and other groups. The present paper focuses on providing solutions to the current problems pertinent to inaccurate matching of counselor positions in ideological and political education, the lagging information feedback, and the existence of imperfect early warning intervention mechanism. The paper proposes an integrated deep learning model which automates the learning of a large number of college students’ user behaviors using deep learning algorithms thereby incorporating early warning classifiers. This helps to establish a model enabling accurate counselor job matching and ideological and political education methods. Simulation is used to verify the effectiveness of the model using relevant databases which establishes the superiority of the proposed method in resolving mismatch issues in human resources, handles imbalances in actual effectiveness, and also ensures lagging information feedback in the process of providing dynamic early warning in case of college and university level ideological and political education.

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In this paper, we used the finite element method to numerically study the possible optical resonance modes in the all-inorganic metal halide perovskite (CsPbX3, [Formula: see text], Br, I) semicircular and circular microcavities. For the semicircular microcavity, the quasi-three-period mode has the highest quality factor among quasi-whispering gallery mode (WGM), two-period mode and four-period mode. The quality factors of the four resonance modes all increase linearly for the larger cavity size. In addition, the circular perovskite microcavities present the WGM patterns with extremely high-quality factors, which indicates that the circular perovskite microcavity has the potential application in the optical microcavity or laser device.