## 3 The Proposed Novel Fuzzy Analytic Hierarchy Process-Based Approach  
With the rapid advancement of science and technology, combining information and Internet platforms has created unprecedented opportunities for upgrading and transforming numerous traditional industries and enterprises. However, traditional Internet platform selection methods cannot handle benefit and non-benefit data simultaneously, nor can they simultaneously handle expert-provided fuzzy semantic information. Moreover, expert-provided data may contain both complete and partly incomplete information, making it more difficult to correctly select the most suitable Internet platform. To address the shortcomings of traditional Internet platform selection approaches, this study combined the FAHP and WASPAS approaches to handle the Internet platform selection issues within an incomplete information environment. The proposed approach utilized the FAHP approach to calculate the subjective weights between different criteria. Defuzzification and data-filling technologies were used to completely consider all expert-provided information for fuzzy semantic and unclear expert-provided information. Finally, the WASPAS approach was applied to calculate the total relative importance of the alternatives and rank the potential alternatives based on the scoring results. The execution process of the proposed novel FAHP-based approach includes the following ten steps.  
Step 1. Confirm the possible alternatives.  
Experts discussed and confirmed all of the possible alternatives.  
Step 2. Confirm the evaluation criteria.  
Experts discussed and confirmed all possible evaluation criteria.  
Step 3. Determine the rating values of possible alternatives under different evaluation criteria.  
Experts determined the rating values of possible alternatives under different evaluation criteria based on their past experience.  
Step 4. Determine the pairwise comparison matrix of different evaluation criteria.  
Experts used the TFN value to determine the pairwise comparison matrix for different evaluation criteria.  
Step 5. Data filling of incomplete information.  
To address the issue of incomplete information in traditional data collection methods, incomplete information was filled in using the average of the information provided by experts who furnish complete data.  
Step 6. Calculate the relative weight of evaluation criteria and perform consistency checks.  
Based on Step 4 and Step 5, Equations (8)-(10) were used to perform consistency checks and to calculate the relative weights of different criteria. The expert opinions were consistent when the CR value was less than 0.1.  
Step 7. Calculate the normalized value of the initial decision matrix.  
According to the initial decision matrix of Step 5, Equations (11) and (12) were used to calculate the normalized value of the initial decision matrix.  
Step 8. Calculate the value of WSM and WPM of different alternatives.  
Based on the results of Step 7, Equations (13) and (14) were used to calculate the \(Q_{i}^{1}\) value and \(Q_{i}^{2}\) value.  
Step 9. Subsequently, the total relative importance of different alternatives was computed.  
According to the \(Q_{i}^{1}\) value and \(Q_{i}^{2}\) value, Equation (15) was used to calculate the \(Q_{i}\) value. The value of \(\lambda\) is usually set to 0.5 when the expert cannot decide the \(\lambda\) value. Step 10. Sort the \(Q_{i}\) value and provide decision-making suggestions.  
The possible alternative sorting results were determined by sorting the \(Q_{i}\) value from largest to smallest.