The study focused on the dual-source computed tomography (CT) images segmented by the decision tree algorithm, to explore the efficacy of docetaxel combined with fluorouracil therapy on gastric patients undergoing chemotherapy. In this study, 98 patients with gastric cancer who were treated in the hospital were selected as the research subjects. The decision tree algorithm was applied to segment dual-source CT images of gastric cancer patients. The decision tree is established according to the feature ring and the segmentation position. The machine inductively learns from the decision tree to extract the features of the CT image to obtain the optimal segmentation boundary. The observation group was treated with docetaxel combined with fluorouracil, and the control group was treated with docetaxel combined with tegafur gimeracil oteracil potassium capsules. The general data of the two groups of patients were comparable and not statistically significant (
P
>
0.05
). The two groups were compared for clinical efficacy, physical status, KPS score, improvement rate, and adverse drug reactions after treatment. The results showed that the improvement rate of physical fitness in the observation group was 38.78%, and the improvement rate in the control group was 18.37%. The total effective rate in the observation group was 42.85%, and the total effective rate in the control group was 36.73%. Obviously, the curative effect and improvement rate of physical fitness in the observation group were significantly better than those in the control group (
P
<
0.05
). In conclusion, the decision tree algorithm proposed in this study demonstrates superb capabilities in feature extraction of CT images. The machine inductively learns from the decision tree to extract the features of the CT image to obtain the optimal segmentation boundary. The effect of docetaxel combined with fluorouracil is better than that of docetaxel combined with tegafur gimeracil oteracil potassium capsules.