Speech fluency is a core indicator of second language proficiency and a critical component of Computer-Assisted Pronunciation Training (CAPT) systems. Accurate assessment requires models to perceive both macroscopic speech flow trends and microscopic local anomalies. However, existing methods struggle to bridge the semantic gap between static expert priors and dynamic temporal representations, while often overlooking the inherent ordinal nature of fluency scores. To address these challenges, we first construct a set of expert features targeting fluency disruptions and rhythmic regularity to provide explicit linguistic priors. Building on this, we propose the Multimodal Multi-Stream Fusion Classification (MMSFC) network. It employs a Mutual Cross-Attention (MCA) mechanism that leverages these expert features as “semantic anchors” to actively guide Whisper’s temporal representations and integrate decoder contexts, achieving deep interaction between global priors and local dynamics. Furthermore, we propose the Ordinal Smoothed Cross-Entropy (OSCE) loss. By constructing distance-aware soft target distributions coupled with confidence-adaptive smoothing and boundary enhancement, OSCE explicitly models ordinal relationships to resolve boundary ambiguity. Experiments on SpeechOcean762 show MMSFC achieves 83.40% accuracy, significantly outperforming strong baselines. Notably, OSCE also demonstrates superior generalization potential in cross-domain CV and NLP tasks. Our code is available at https://github.com/speech26ai/MMSFCCode.