Green intelligent building materials is an effective way for building materials industry to reduce carbon. However, a small amount of research and development (R&D, unstable R&D investment and imperfect collaborative innovation mode hinder the development of green intelligent building materials industry. However, few scholars study the development mechanism of green intelligent building materials industry from the perspective of industrial chain considering the above obstacles. In this study, the game models under market mechanism and government regulation were constructed to analyze the income distribution mechanism for the development mechanism of green intelligent building materials industry. Finally, the questionnaire method was used to discuss the game strategy of collaborative innovation behavior among agents. The results are as follows. In the game strategy selection of collaborative innovation behavior among green intelligent building materials, factors such as database marketing maturity, information flow and technology volume generated by collaborative innovation, technical benefit coefficient, social benefit coefficient and profit and loss barrier factors are conducive to the collaborative innovation behavior of green intelligent building materials. When the market mechanism fails, the incentive effect of cost subsidy adopted by the government is more efficient and fast, and the driving force of achievement reward is more lasting. The combination of the two incentives is the best. Moderate supervision and punishment lower than the free rider income can not ensure fair competition among green intelligent building materials enterprises. The punishment above the threshold can effectively restrain the negative impact of free rider income and prospect profit and loss. This study not only theoretically expands the development theory of digital industry from the perspective of industrial chain by considering the maturity factor of database, but also provides policy guidance for the development of green intelligent building materials industry in practice.

Circular intuitionistic fuzzy sets (C-IFS) have lately been regarded as a modified form of intuitionistic fuzzy sets (I-FS). But they are regarded solely for certain elementary algebraic characteristics. This study enhances C-IFS in Dombi aggregation operators (AOs) by including more mathematical characteristics of algebraic laws. The operators encompass C-IF Dombi weighted averaging (C-IFDWA), C-IF Dombi ordered weighted averaging (C-IFDOWA), C-IF Dombi weighted geometric (C-IFDWG), and C-IF Dombi ordered weighted geometric (C-IFDOWG). Furthermore, we delineate the characteristics of idempotency, monotonicity, and boundedness for the suggested operators. Therefore, to calculate the primary component, we determine the multi-attribute decision-making (MADM) technique utilizing the offered operators for C-IF values (C-IFVs) to augment the quantity of the assessed operators. The proposed approach is validated through a numerical example, illustrating the advantages in terms of flexibility and accuracy. The findings contribute to the advancement of decision-making tools for selecting energy storage systems, offering a robust framework for future applications in the field.