Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaints remains a difficult problem. Current multi-label classification methods applied to complaint texts have not been able to fully utilize complaint information, and little research has been performed on complaint classification in the aviation industry. Therefore, to solve the problems of insufficient text feature extraction and the insufficient learning of inter-feature relationships, we constructed a multi-label classification model (MAG, or multi-feature attention gradient boosting decision tree classifier) for civil aviation service quality complaint texts. This model incorporates multiple features and attention mechanisms to improve the classification accuracy. First, the BERT (Bidirectional Encoder Representations from Transformers) model and attention mechanisms are used to represent the semantic and label features of the text. Then, the Text-CNN (a convolutional neural network) and BiLSTM (bidirectional long short-term memory) multi-channel feature extraction networks are used to extract the local and global features of the complaint text, respectively. Subsequently, a co-attention mechanism is used to learn the relationship between the local and global features. Finally, the travelers’ complaint texts are accurately classified by integrating the base classifiers. The results show that our proposed model improves the multi-label classification accuracy, outperforming other modern algorithms. We demonstrate how the label feature representation based on association rules and the multi-channel feature extraction network can enrich textual information and more fully extract features. Overall, the co-attention mechanism can effectively learn the relationships between text features, thereby improving the classification accuracy of the model and enabling better identification of travelers’ complaints. This study not only effectively extracted text features by integrating multiple features and attention mechanisms, but also constructed a targeted feature word set for complaint texts based on the domain-specific characteristics of the civil aviation industry. Furthermore, by iterating the basic classifier using a multi-label classification model, a classifier with higher accuracy was successfully obtained, providing strong technical support and new practical paths for improving the civil aviation service quality and complaint management.

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports.

The civil aviation industry has always been an important strategic industry for China's economic and social development. The increasing popularity of air travel has made passenger needs more diverse, which has made passengers pay more attention to the overall service quality of aviation. This change makes civil aviation service need to pay attention to risk management, need to change from post-management to pre-management, so it can also improve the level of service quality. The civil aviation industry is relatively mature in safety risk research and control technology, but civil aviation enterprises are in the initial stage for service risk. FMEA(Failure Mode and Effects Analysis) is widely used in aviation manufacturing, finance, automotive industry, medical industry and other industries. Combined with the characteristics of civil aviation service process, this study discusses how to use FMEA to manage civil aviation service risk. The application example of an airport proves that FMEA tool can improve the airport service risk management and the service level. FMEA can be applied to service risk management in the entire civil aviation industry.

Green flight service uses incentive mechanisms to encourage passengers to participate in low-carbon value co-creation. However, willingness to make green choices does not necessarily translate into smooth participation, as service failures during implementation may create barriers to passenger participation. Drawing on Service System Theory, this study conceptualizes green flight as a service system involving passengers, airlines, service rules, and incentive mechanisms. Using 1161 passenger complaints collected from the Service Quality Supervision Platform of the Civil Aviation Administration of China between January 2021 and June 2025, Word2Vec-based semantic expansion and the Structural Topic Model (STM) were applied to identify service failures and corresponding recovery demands. The results show that major participation barriers include restricted service choice, inadequate information disclosure, imbalances between service rules and passenger rights and interests, service execution flaws, and failures in reward fulfillment and service response. Rigid rules restricting passengers’ ability to modify green flight options under irregular flight conditions represent the most prominent issue. These problems also show dynamic changes over time. Correspondingly, passengers’ recovery demands range from immediate remedies to systematic optimization, including responsibility-taking and rule improvement, compensation, explanations and notification, timely processing, reward fulfillment, service attitude improvement, and stronger internal governance. The findings indicate that sustainable service adoption depends not only on environmental willingness but also on whether the service system reduces participation costs and uncertainty. This study develops a “service failure–passenger demand–service recovery” framework for passenger-participatory sustainable services.