Process models represent valuable experiential knowledge accumulated by enterprises throughout their development. The reuse of such knowledge can save managerial time and enhance the efficiency of process designers. Traditional process recommendation methods mostly focus on matching between process models themselves while neglecting the cross-modal matching problem between process texts and process models. Furthermore, enterprises accumulate process models in diverse forms, yet existing methods can only recommend a single form of process model (e.g., BPMN or Petri nets) and are unable to recommend multiple forms simultaneously. To address these challenges, this paper proposes a novel process recommendation network, referred to as ProcNet, which integrates large language models (LLMs) and graph convolutional networks (GCNs) for process recommendation. Specifically, (1) ProcNet uses LLMs to extract process activities and their relationships from requirement texts, constructs a directed graph, and semantically aligns it with the process activities in the database. Meanwhile, the process models in the database are also transformed into directed graphs; (2) ProcNet employs GCNs to obtain high-dimensional feature representations of each directed graph; (3) ProcNet calculates the top-k process models in the database most similar to the requirements using Euclidean distance and recommends them to the user. Extensive experiments demonstrate that ProcNet outperforms existing baselines, achieving remarkable results in recall, precision, F1-score, and mean average precision (MAP).