It introduces a prediction method for the transformation of consumption behavior of educational institutions, namely, the consumption behavior of educational institutions under the LSTM (long-term short-term memory) network. Based on the theory of behavior, this paper predicts the decision-making of consumers, and uses statistical factor analysis and structural equation model to establish the analysis model of the demographic factors affecting the purchase of educational institutions. Relevant data are collected, and software such as SPSS is used for analysis and modeling. Through related tests, the preferences and related latent variables of a certain type of group are revealed. Multi-dimensional analysis, accurate prediction, and one-to-one recommendation are necessary ways for educational institutions to obtain profits and improve consumers’ satisfaction.

Guanghui Yang, Puyang Zheng. A prediction method of consumer behavior transformation in K12 educational institutions. Frontiers in Educational Research (2021) Vol. 4, Issue 6: 64-73. https://doi.org/10.25236/FER.2021.040613.

[1] Behera R K, Jena M, Rath S K, et al. Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Information Processing & Management, 2021, 58(1),pp.102435.

[2] Bellini S, Cardinali M G, Grandi B. A structural equation model of impulse buying behaviour in grocery retailing. Journal of Retailing and Consumer Services, 2017, 36,pp.164-171.

[3] Tarka P. An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Quality & quantity, 2018, 52(1),pp.313-354.

[4] Xu F, Chen H. The Effects of Consumer Online and Offline Purchasing Behavior on Real Retailer’s Business Strategy: An Analysis based on the Multi-group Structural Equation Model. Commercial Research, 2017, (6), pp.17-23, 85.

[5] Li Y F, Cao H. Prediction for tourism flow based on LSTM neural network. Procedia Computer Science, 2018, 129, pp.277-283.

[6] Hu W, Shi Y. Prediction of online consumers’ buying behavior based on LSTM-RF model.2020 5th International Conference on Communication, Image and Signal Processing (CCISP). IEEE, 2020:,pp.224-228.

[7] Huang H, Zhao B, Zhao H, et al. A cross-platform consumer behavior analysis of large-scale mobile shopping data. Proceedings of the 2018 World Wide Web Conference. 2018,pp.1785-1794.

[8] Thomas N T. A LSTM based Tool for Consumer Complaint Classification.2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018,pp.2349-2351.

[9] Kim J, Moon N. LSTM-Based Consumption Type Prediction Model. Advances in Computer Science and Ubiquitous Computing. Springer, Singapore, 2018,pp.564-567.

[10] Zhou H, Wang D. Study on Purchase Intention of Green Vegetable Consumers Based on Structural Equation Model. Northern Horticulture,2019,(15),pp.157-163.

[11] Liu Z, Yin L, Yuan L. Research on the Impact of Logistics Service Factors on Consumers’ Online Shopping Intention Based on Structural Equation Modeling. Mathematics in Practice and Theory,2019,49(4),pp.34-42.

[12] Yang Y, Ji L, and Chen H,et al. Study on the Influence of Chateau Experience on Customers ‘Purchasing Behavior Based on the Structural Equation Model of Jiaodong Area. Liquor-Making Science & Technology.2020,(10),pp.129-134.

[13] Lv Y, Liu Q, Jiang C. Sentiment analysis measure of topic mapping in LDA-LSTM combination model. IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2021, 702(1),pp. 012015.

[14] Zhang Q, Gao T, Liu X, et al. Public environment emotion prediction model using LSTM network. Sustainability, 2020, 12(4),pp.1665.

[15] Li Y F, Cao H. Prediction for tourism flow based on LSTM neural network. Procedia Computer Science, 2018, 129,pp.277-283.

[16] Rafik M, Fentis A, Khalili T, et al. Learning and Predictive Energy Consumption Model based on LSTM recursive neural networks.2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS). IEEE, 2020,pp.1-7.

[17] Lin X M, Ho C H, Xia L T, et al. Sentiment analysis of low-carbon travel APP user comments based on deep learning. Sustainable Energy Technologies and Assessments, 2021, 44:,pp.101014.

[18] Thomas N T. A LSTM based Tool for Consumer Complaint Classification.2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018,pp.2349-2351.

[19] Pawaskar U S, Raut R D, Gardas B B. Assessment of consumer behavior towards environmental responsibility: A structural equations modeling approach. Business Strategy and the Environment, 2018, 27(4),pp.560-571.

[20] Kim D, Ammeter A P. Shifts in online consumer behavior: a preliminary investigation of the net generation. Journal of theoretical and applied electronic commerce research, 2018, 13(1),pp.1-25.

[21] Simanjuntak M, Nur H, Sartono B, et al. A general structural equation model of the emotions and repurchase intention in modern retail. Management Science Letters, 2020, 10(4),pp. 801-814.

[22] do Paco A, Shiel C, Alves H. A new model for testing green consumer behaviour. Journal of cleaner production, 2019, 207,pp.998-1006.

[23] Dachyar M, Banjarnahor L. Factors influencing purchase intention towards consumer-to-consumer e-commerce. Intangible Capital, 2017, 13(5),pp.946-966.

[24] Wang M. Consumer Behavior Analysis in the Offline Retail Stores based on convolutional neural network. Journal of Physics: Conference Series. IOP Publishing, 2020, 1544(1),pp.012162.

[25] Safitri A, Purnomo H D. Consumer Behavior Analysis using Apriori Algorithm. International Journal of Information Technology and Business, 2019, 1(2),pp.5-5.

[26] Zhang C, Tan T. The Impact of Big Data Analysis on Consumer Behavior.Journal of Physics: Conference Series. IOP Publishing, 2020, 1544(1),pp.012165.

[27] Seo S Y, Kim S D, Jo S C. Utilizing Case-based Reasoning for Consumer Choice Prediction based on the Similarity of Compared Alternative Sets. The Journal of Asian Finance, Economics, and Business, 2020, 7(2), pp.221-228.

[28] An H, Choi Y, Oh R, et al. Consumer behavior prediction using Airbnb web log data. The Korean Journal of Applied Statistics, 2019, 32(3),pp.391-404.

[29] Liu D, Sun Y, Qu Y, et al. Analysis and accurate prediction of user’s response behavior in incentive-based demand response. IEEE Access, 2018, 7,pp.3170-3180.

[30] Bashir S, Khwaja M G, Turi J A, et al. Extension of planned behavioral theory to consumer behaviors in green hotel. Heliyon, 2019, 5(12),pp.02974.