Aiming at the shortcomings of existing methods for predicting remaining life of small-sample high-reliability equipment， such as low accuracy and poor prediction generalization， a new assessment method is proposed. This method uses a jointly trained high-order neighborhood aggregation graph convolutional neural network and a bidirectional gated recurrent unit （GRU）. This method constructs an attribute graph from large public datasets combined with small sub-sample information， such as equipment’s key electrical potentials. The HoNA-GCN model integrates different orders of neighbor information to capture the high-order associative features between equipment samples. Life prediction is then carried out using the bidirectional GRU， with a pretraining-finetuning strategy employed to enhance the model's generalization capabilities. The precision of equipment residual life prediction has been improved， along with generalization across different scenarios. Simulations and ablation studies validate the necessity of each module of the method. Results indicate a significant enhancement in the accuracy and robustness of predictions compared to those of other classic methods. By effectively utilizing the correlation information between public datasets and small sub-sample data of equipment， a new solution is offered for the state assessment of complex equipment systems.