###### Abstract
The former has clear boundary values, but the specific value within the range is unclear, represented as \"uncertain connotation, clear extension\", while the latter can only be expressed using fuzzy language such as \"good\" or \"better\", and the boundaries between \"good\" and \"better\" are unclear, represented as \"clear connotation, unclear extension\". Therefore, it is important to separate the descriptions of quantitative and qualitative attributes. Therefore, considering the limitations of both direct and indirect methods, it is highly suitable and effective to use the multi-attribute group decision-making (MADM) method for electricity sales package recommendations. Multi-attribute decision-making has always been a focus in decision science. In 1979, [PERSON] et al. introduced prospect theory and the concept of \"reference points\" [26]. Prospect theory highlights the psychological bias in human perception when facing risks, where individuals tend to \"overestimate low-probability events and underestimate high-probability events\". This theory can explain many phenomena that traditional expected utility theory cannot, and when applied to multi-attribute decision-making, it effectively addresses the preference issues of decision-makers. For example, in reference [27], the cumulative prospect theory and the VIKOR method are incorporated into the Pythagorean hesitant fuzzy risk multi-attribute decision-making model to address bounded rational behavior among customers. In reference [28], the hesitant fuzzy set prospect theory evaluation method is used to compare evaluation index values under various scenarios, mitigating the impact of individual decisions on corporate emissions reduction, and identifying the recommended energy reduction plan. Reference [29] proposes an electricity sales package recommendation method based on the prospect advantage- disadvantage degree and Choquet integral. The reference point, as the basis for decision-makers' judgment and choices, directly influences the decision outcome. Currently, research on reference point setting in multi-attribute decision-making can be divided into two main categories. One category assumes that reference points exist by default within the decision-making method itself. For example, in the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, the positive and negative ideal points serve as reference points. Additionally, methods like the Analytic Hierarchy Process (AHP), Data Envelopment Analysis (DEA), Weighted Sum Method, Weighted Product Method, and Elimination and Choice Translating Reality (ELECTRE) all implicitly incorporate the idea of reference points [30]. Inthese methods, although reference points are not explicitly mentioned, they are assumed to exist objectively and play a role. The other category involves decision-makers subjectively setting reference points. In references [31; 32], the authors use decision-makers' expected values as reference points and propose multi-attribute decision-making methods based on prospect theory. From the above research, it is evident that studies on reference points in multi-attribute decision-making are relatively scarce, and the setting of reference points is usually performed in static decision-making scenarios. However, in dynamic multi-attribute decision-making problems, reference points often change dynamically over time, along with the continuous evolution of the decision environment. Therefore, it is necessary to set a dynamic reference point that is related to time. However, current research on the dynamic reference point setting in multi-attribute decision-making is almost non-existent. To address this, this paper proposes a new electricity sales package recommendation method--dynamic hybrid multi-attribute group decision-making with two reference points for electricity sales package recommendation (DH-MAGDM).