This paper presents an in-depth analysis and study of the diagnostic effectiveness of EUS-RTE in giant cystic tumours of the oesophagus utilizing cluster analysis. A new form of interval data expression was designed based on the cluster analysis algorithm, as well as a new way of updating the cluster radius and cluster centre. Feature triads are defined, eliminating the need to access all historical data at the time of update. It also prevents the case of overfusion of clusters and outputting only one cluster. If there exist a very low number of clusters, the newly merged clusters are reclustered according to the density clustering method for the internal data objects based on the cluster segmentation so that the data objects in the same cluster have a high similarity as possible. All accumulated electronic files of oesophageal cancer cases were collected and comprehensively organized, and all clinical data of 129 eligible cases with a total of 356 consultations were screened in strict accordance with inclusion and exclusion criteria. A database of oesophageal cancer cases was established using Visual FoxPro software, and frequency distribution, cluster analysis, association rule, and chi-square test were used to focus on mining the association between symptoms, disease mechanisms, prescriptions, and medications. The results were analysed and summarized. Overall, the therapeutic efficacy and safety of the three groups of treatment modalities for gastric mesenchymal tumours were positive, and the preoperative endoscopic treatment modalities should be selected based on the EUS-RTE characteristics of the tumour, the site, and the operator’s skill level in a comprehensive manner.