In order to improve the ideological and political education of college students, this paper constructs a cloud technology education platform based on big data analysis and collection technology. Firstly, the collaborative filtering algorithm is used to filter and collect student information; secondly, the Pearson correlation formula is used for pre-processing, MAE (mean absolute error value) is used as an evaluation index; and finally, the data is combined with FCM (fuzzy C-mean) algorithm to filter and analyze as needed. Its application performance is examined to verify the rationality and practicality of the construction of the cloud technology education platform. The analysis results show that, compared with the three-tier neural network education platform and MOOC (Massive Open Online Course) education platform, the educational resource transmission time of the cloud technology education platform constructed in this paper is reduced by 1/3, and the load balancing deviation is reduced by 1/4. Among four different servers, the average error value of the platform constructed in this paper is as low as 0.35% and can reach as high as 0.65%. The detection rate reaches 97.64%, the false alarm rate is only 2.93%, and the leakage rate is only 1.13%. It can be seen that the cloud technology education platform constructed in this paper can improve the utilization rate of educational resources and realize the sharing of educational resources.

The development of vocational education in the information age requires us to think about the path and strategy of active change. Course teaching quality evaluation should also shift from passive evaluation of online teaching development to active construction of a mixed teaching quality evaluation system. In the information age, the development of teaching resources is dizzying. From paper to digital, from single to diverse, from offline to online, from scarcity to mass—various changes impact the traditional teaching model. Aiming at the online teaching quality evaluation of international Chinese education on the Internet, this paper proposes a method based on deep learning. Firstly, this paper proposes an index system construction and evaluation index weighting for online teaching of international Chinese education, and collects online data as a corpus at the same time. Then construct the CNN_BiLSTM_Att model, which is composed of the CNN module, the BiLSTM module and the Att module. Finally, compare with other model experiments. The experimental results show that CNN_BiLSTM_Att has achieved the best results in the evaluation index results, with P and F1 reaching 97.89% and 97.85%. Compared with other models, the overall effect is improved by 2%~5%. From this, the superiority of the model in the online teaching quality evaluation standard task of this paper can be obtained.

Scientific and technological innovation is the source of the survival and development of enterprises and the key to the realization of the goal of prosperity. In recent years, more and more companies have begun to focus on technological innovation, but the results are not significant, so companies have begun to explore the factors that affect their technological innovation. The management is the helm of the development of the enterprise, the main body of the company’s actual production activities, and the direct person in charge of the company’s management. Its influence on the innovation of the enterprise is self-evident, and the education level of the management directly determines the manager’s ability and vision. However, the current research on management mainly focuses on the position change of management and the rights of management and does not involve the level of education of management. Based on this, this article started from the management education and subdivided it with the K-means clustering algorithm, so as to explore the impact of management education on the technological innovation of enterprises. The experiment showed that there was a significant positive correlation between the educational level of management and the technological innovation ability of enterprises, and the correlation coefficient was 1.521. It fully shows that the management with a higher education background will promote the enterprise to carry out scientific and technological innovation practice and continuously improve the enterprise’s innovation ability.