Vascular invasion (VI) is one of the important risk factors for poor prognosis of colorectal cancer (CRC), and there is an urgent need to develop methods to predict the risk of VI for CRC patients. A more efficient and intuitive prediction model based on artificial neural networks (ANN) was developed to improve the prognosis of patients with CRC and to assist clinicians in the decision-making process. 151 patients diagnosed with CRC were included. The ANN was used to predict the VI status of 12 clinical and pathological features. Of all the risk characteristics, N stage had the largest cumulative effect. The multi-feature joint assessment distinguished patients with VI from those without, with an AUC (area under curve) of 0.87 in the training cohort and 0.80 in the verification cohort. The application of an ANN-based individualized prediction model that could be used to identify patients at high risk of VI and to personalize follow-up plans for such patients may improve outcomes for CRC patients.