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Anemia is a global health issue that adversely affects the body. Currently, the mainstream method for anemia detection involves collecting blood samples, which may cause discomfort due to skin puncture. Therefore, this article proposes an improved ResNet34 anemia detection algorithm. By enhancing residual blocks and introducing overall skip connections within residual block groups, the classification accuracy is effectively improved. Additionally, a non-invasive anemia detection WeChat mini-program is developed, featuring simple operation and convenient use, enabling anemia detection anytime and anywhere. It is suitable for users" daily lives. Experimental results in real-world settings demonstrate that the system achieves correct classification for all test samples. Its usability and accuracy largely meet user requirements, offering valuable insights for non-invasive anemia detection.