In order to make the construction of ecological civilization conform to the objective laws and the world trend, adapt to the requirements of social development, build a new pattern of harmonious coexistence between man and nature, and accelerate the process of ecological civilization construction. Promoting the construction of ecological civilization is an irreplaceable fundamental guarantee for the sustainable development of the Chinese nation and a strategic decision made by China to meet the requirements of future development. Realizing sustainable development through ecological environment protection is one of the important principles of socialism with Chinese characteristics, and is also the essential requirement of the socialist system with Chinese characteristics. Effective measures are taken to solve the outstanding difficulties and problems facing mankind. Therefore, we must scientifically and correctly recognize the important and realistic objective existence of ecological value.

Saurabh Kumar. Ecological Value of Natural Protection Environment Considering Data Mining. Nature Environmental Protection (2021), Vol. 2, Issue 4: 10-18. https://doi.org/10.38007/NEP.2021.020402.

[1] Mengwei Zhao, Guiwu Wei, Cun Wei, Jiang Wu, Yu Wei. Extended CPT-TODIM Method for Interval-Valued Intuitionistic Fuzzy MAGDM and Its Application to Urban Ecological Risk Assessment. J. Intell. Fuzzy Syst. (2021) 40(3): 4091-4106. https://doi.org/10.3233/JIFS-200534

[2] Sheng-Xu Shi. Performance Evaluation of Urban Ecological Environment Construction with Interval-Valued Intuitionistic Fuzzy Information. J. Intell. Fuzzy Syst. (2017) 32(1): 1119-1127. https://doi.org/10.3233/JIFS-16475

[3] Bokolo Anthony Jr. Green Information Systems Refraction for Corporate Ecological Responsibility Reflection in ICT Based Firms: Explicating Technology Organization Environment Framework. J. Cases Inf. Technol. (2020) 22(1): 14-37. https://doi.org/10.4018/JCIT.2020010102

[4] Irani Hazarika, Anjana Kakoti Mahanta. Mining Maximal Frequent Rectangles. Adv. Data Anal. Classif. (2021) 16(3): 593-616. 

[5] Ali Hamdi, Khaled B Shaban, Abdelkarim Erradi, Amr Mohamed, Shakila Khan Rumi, Flora D Salim. Spatiotemporal Data Mining: A Survey on Challenges and Open Problems. Artif. Intell. Rev. (2021) 55(2): 1441-1488. 

[6] Clarisse Dhaenens, Laetitia Jourdan. Metaheuristics for Data Mining: Survey and Opportunities for Big Data. Ann. Oper. Res. (2021) 314(1): 117-140. 

[7] Yi-Kuei Lin, Shin-Guang Chen. Reliability Evaluation in Terms of Flow Data Mining for Multistate Networks. Ann. Oper. Res. (2021) 311(1): 225-237. https://doi.org/10.1007/s10479- 020-03774-7

[8] Tin C Truong, Hai V Duong, Bac Le, Philippe Fournier-Viger, Unil Yun. Frequent High Minimum Average Utility Sequence Mining with Constraints in Dynamic Databases Using Efficient Pruning Strategies. Appl. Intell. (2021) 52(6): 6106-6128. https://doi.org/10.1007/s10489-021-0 2520-1

[9] Tamil Selvi M, Jaison B. Lemuria: A Novel Future Crop Prediction Algorithm Using Data Mining. Comput. J. (2021) 65(3): 655-666. https://doi.org/10.1093/comjnl/bxaa093

[10] Jialin Ma, Zhaojun Wang, Hai Guo, Qian Xie, Tao Wang, Bolun Chen. Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data. Comput. Syst. Sci. Eng. (2021) 40(3): 979-993. https://doi.org/10.32604/csse.2021.016759

[11]Durgesh Samariya, Jiangang Ma. A New Dimensionality-Unbiased Score for Efficient and Effective Outlying Aspect Mining. Data Sci. Eng. (2021) 7(2): 120-135. 

[12] Esther Galbrun. The Minimum Description Length Principle for Pattern Mining: A Survey. Data Min. Knowl. Discov. (2021) 36(5): 1679-1727. 

[13] Steedman Jenkins, Stefan Walzer-Goldfeld, Matteo Riondato. SPEck: Mining Statistically-Significant Sequential Patterns Efficiently with Exact Sampling. Data Min. Knowl. Discov. (2021) 36(4): 1575-1599. 

[14] Antonio Longa, Giulia Cencetti, Bruno Lepri, Andrea Passerini. An Efficient Procedure for Mining Egocentric Temporal Motifs. Data Min. Knowl. Discov. (2021) 36(1): 355-378. https:// doi.org/10.1007/s10618-021-00803-2

[15] Tatiana Makhalova, Sergei O Kuznetsov, Amedeo Napoli. Mint: MDL-Based Approach for Mining INTeresting Numerical Pattern Sets. Data Min. Knowl. Discov. (2021) 36(1): 108-145. https://doi.org/10.1007/s10618-021-00799-9

[16] Chitta Ranjan, Samaneh Ebrahimi, Kamran Paynabar. Sequence Graph Transform (SGT): A Feature Embedding Function for Sequence Data Mining. Data Min. Knowl. Discov. (2021) 36(2): 668-708. https://doi.org/10.1007/s10618-021-00813-0

[17] P Revanth Rathan, P Krishna Reddy, Anirban Mondal. A Framework for Discovering Popular Paths Using Transactional Modeling and Pattern Mining. Distributed Parallel Databases. (2021) 40(1): 109-133. https://doi.org/10.1007/s10619-021-07366-7

[18] Mohammad Arfaee, Arman Bahari, Mohammad Khalilzadeh. A Novel Prediction Model for Educational Planning of Human Resources with Data Mining Approach: A National Tax Administration Case Study. Educ. Inf. Technol. (2021) 27(2): 2209-2239. https://doi.org/10. 1007/s10639-021-10699-6