In recent years, aspect-based sentiment analysis utilizing graph neural networks to exploit syntactic information has become increasingly popular. However, existing methods often overlook the impact of different types of relations on content words, making it challenging to distinguish key relational terms. In addition, the mutual supplementation of multi-perspective information plays a crucial role in capturing sentiment features, but integration mechanisms have often been neglected in past research. To address these issues, a multi-source information graph convolutional network (MSI-GCN) is proposed to effectively capture and integrate three perspectives of information. First, a dual-channel information extraction module, syntax-semantics dual graph convolutional network (SSD-GCN), is designed, comprising a type-enhanced syntax graph convolutional network (TES-GCN) and a semantic graph convolutional network (SEM-GCN). TES-GCN enhances syntactic information by incorporating a type embedding layer and learning different weights through the syntactic module. SEM-GCN encodes self-attention matrices to capture semantic information and employs orthogonal regularization to strengthen semantic associations. Second, external knowledge graph embeddings enrich vocabulary features. Finally, a local-global convolutional network is introduced to leverage the complementarity between perspectives for effective integration. The proposed method is evaluated on four public datasets, showing improvements in accuracy and Macro-F1 scores compared to baseline models.