###### Abstract  
Given that existing electricity sales package recommendation methods mainly consider scenarios where customers are familiar with all the attributes of the packages, they tend to overlook the mixed nature of quantitative and qualitative attributes, psychological factors, and the determination of attribute weights in the decision-making process. To address these limitations, this paper proposes a dynamic hybrid multi-attribute group decision-making with two reference points for electricity sales package recommendation, combining prospect theory. The specific approach is as follows: First, the AAP clustering technique is used to divide the sample customer set and establish a customer load information database, from which customer characteristic profiles are extracted to identify sample customers similar to the target customer. Second, considering the mixed nature of quantitative and qualitative attributes of electricity sales packages and the different characteristics of these attributes, the three-parameter interval gray number and triangular fuzzy number are used to characterize them. A mixed target model is proposed as an external reference point. Next, time reference points are set by using the mean and average development speed to examine the dynamic development of the electricity sales packages. Then, the vector angle method is used to assess the similarity between individual decision-making and group decision-making, establishing an appropriate customer weight adjustment model. The comprehensive prospect value of each package is calculated under the two reference points by comparing the values, which are then used to rank all electricity sales packages. Finally, a case study of customers from a region in China is conducted to verify the accuracy and effectiveness of the proposed recommendation method. e 2025 15 2331 2025  
driven by internet thinking in a highly competitive market and are a necessary means for power retail companies to enhance economic efficiency and gain market competitiveness. As a vehicle for delivering retail services, the competitiveness of electricity sales packages is a key issue that retail companies must address to establish a foothold in the market. Studies indicate that, since 2007, the German electricity market has offered customers more than 9000 electricity sales packages [2; 3; 4]. In Texas, USA, over 1820 fixed-rate electricity sales packages and corresponding pricing strategies have been provided through specific platforms since the development of electricity market reforms [5; 6; 7]. Similarly, following market reforms, the UK introduced various time-of-use electricity pricing packages for customer selection [8]. In China, electricity retail companies have also designed diverse electricity pricing packages targeting both industrial and commercial users [9] as well as residential customers [10]. Reference [11] introduces China's first personalized customization service platform, which offers tailor-made packages to customers. However, in market-based transactions, the wide variety and complexity of retail electricity packages pose challenges for customers in identifying suitable options, leading to increased information costs associated with their decision-making process. Under these circumstances, to enhance market share, profitability, and competitiveness, electricity retail companies must adopt accurate and scientific methods to recommend customized electricity sales packages that precisely meet customers' needs. Currently, existing electricity sales package recommendation methods can be divided into two types: direct recommendation methods and indirect recommendation methods. The direct recommendation rule for electricity sales packages is based on online analysis platforms that recommend packages by matching customer information with existing electricity sales packages according to the similarity of electricity usage, selecting the most economical solution to recommend to customers.