With the promotion of the new round of industrial revolution, the development environment of enterprises has undergone tremendous changes. Collaboration among enterprises has become crucial for enhancing core competitiveness, with the concept of the supply chain playing a key role. However, the complex and vulnerable nature of the supply chain operation environment poses various risks, hampering effective cooperative relationships between enterprises. This article proposed integrating the partial least-squares-artificial neural network (PLS-ANN) method to address this issue and optimize collaborative enterprise practices. The study examined enterprise collaboration optimization. This article uses used artificial neural network (ANN) to classify various complex data, implement an intelligent algorithm model for synchronous processing, and combine partial least squares (PLS) to classify and process the data information generated by collaborative networks to find the best match, minimizing the negative impact of multiple correlations of variables on enterprise collaboration. An empirical analysis was conducted in 2022, focusing on a manufacturing enterprise's supply chain and external cooperation management. The analysis examined two aspects: the supply chain's risk resistance level and the effectiveness of enterprise cooperation. Results showed that after implementing the PLS-ANN model, the average trust index between the enterprise and eight cooperative partners increased to approximately 0.652, compared to the initial average trust index of only 0.528. Detailed data analysis indicated that the PLS-ANN method effectively improved the supply chain's risk resistance capability while optimizing the cooperative relationships among all participating enterprises.