Industrial chain risk assessment and early warning are essential measures to effectively protect the interests of upstream and downstream companies in the industrial chain and mitigate company risks. However， existing methods often need to pay more attention to the propagation effects between upstream and downstream companies in the industrial chain and the opacity of company information， resulting in inaccurate risk assessment of companies and the failure to perceive risks in advance for early warning. To address the above problems， the Hierarchical Graph Neural Network （HiGNN）， an industrial chain risk assessment and early warning model that combined Hierarchical Graph （HG） neural network and Long Short-Term Memory （LSTM）， was proposed. Firstly， an “industrial chain-investment” HG was constructed based on the relationships between upstream and downstream companies and investment activities. Then， a financial feature extraction module was utilized to extract features from multi-quarter financial data of companies， while an investment feature extraction module was utilized to extract features from the investment relationship graph. Finally， an attention mechanism was employed to integrate the financial features with the investment features， enabling risk classification of company nodes through graph representation learning methods. The experimental results on a real integrated circuit manufacturing dataset showed that compared with the Graph ATtention network （GAT） model and the Recurrent Neural Network （RNN） model， the accuracy of the proposed model increased by 14.87% and 22.10%， and the F1-score increased by 12.63% and 16.67% with the 60% training ratio. The proposed model can effectively capture the contagion effect in the industrial chain and improve risk identification capability， which is superior to traditional machine learning methods and graph neural network methods.