The existing stock prediction models are mainly based on unimodal， and ignore inter-industry linkage effects and information heterogeneity. Although some studies have introduced textual modalities， they still struggle with challenges such as time lag effects and multi-granularity caused by modality inconsistency. Therefore， MATCH（Multimodal stock prediction frAmework inTegrating time-frequenCy features and Hybrid text）， a multimodal fusion framework for stock prediction that integrates heterogeneous information across modalities effectively was proposed. Specifically， a Mixture of Experts （MoE） pretraining strategy was designed to build industry-specific pretrained models of representations， so as to select matched expert networks dynamically and incorporate industry features information. At the same time， a frequency-domain decomposition and hierarchical fusion mechanism was designed to jointly model temporal patterns at multiple frequencies， and a dual-stream pretraining architecture was used to obtain representations of high-frequency future fluctuations and low-frequency future trends， which were interacted with text information across multiple time scales cross-modally， thereby capturing market dynamics more precisely and enabling effective interaction between time-series and textual data in multi-granular scenarios. Experimental results on two real-world stock datasets， S&P 500 and CMIN-US for comparing MATCH and mainstream methods such as ESTIMATE （Efficient STock Integration with teMporal generative filters and wavelet hypergraph ATtEntions） and PatchTST demonstrate that， on S&P 500 dataset， MATCH has the Sharpe Ratio （SR） improved by 50.5% over the sub-optimal baseline model Adv-ALSTM， while on the more challenging CMIN-US dataset， MATCH achieves a 2.35% SR improvement， with other metrics reaching the best. It can be seen that MATCH provides a novel and efficient solution for multimodal data fusion in finance.