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In response to the low accuracy of jujube variety recognition in current natural scenarios, a jujube variety recognition model was proposed based on attention mechanism and multi-semantic feature enhancement (ICBAM_MSFE_Res50). On the basis of ResNet-50, the attention mechanism ICBAM (improved convolutional block attention module) was introduced. ICBAM improved the convolutional block attention module (CBAM) by using one-dimensional convolution and multi-scale hole convolution, eliminating information loss during feature map dimensionality reduction, reducing the computational and parameter complexity of the model, and improving the model’s ability to extract fine-grained features in jujube fruit regions. At the same time, a multi-semantic feature enhancement (MSFE) module was proposed, which extracted more local salient features of jujube fruit through jujube fruit region localization algorithm, and used saliency feature suppression algorithm to force the model to learn secondary features of jujube fruit, thereby achieving the learning of multiple semantic features of jujube fruit. The experimental results showed that the accuracy of the model on the dataset of 20 types of jujube varieties was 92.20%, which was 4.26 percentage points higher than that of ResNet-50. Compared with the AlexNet, VGG-16, ResNet-18, and InceptionV3 models, the accuracy was improved by 15.84, 9.22, 6.86, and 3.55 percentage points, respectively. Compared with other jujube variety recognition methods, this method still performed the best in the recognition of 20 types of jujube, which can provide reference for research on jujube variety recognition in natural scenarios.