Fetal cardiac ultrasound examination plays a pivotal role in the prenatal diagnosis of congenital heart disease，which is one of the most prevalent and serious birth defects worldwide. However，clinical acquisition of fetal heart standard planes is highly challenging because of several physiological and technical constraints. The fetal heart is small in size，has a high beating rate，and its position in the maternal abdomen is easily influenced by fetal posture and movement. As a result，capturing the five clinically required standard planes，such as the four-chamber and outflow tract views，demands high temporal precision and operator experience. Moreover，the acquisition process suffers from poor repeatability，further hindering consistent and efficient screening outcomes. These challenges limit the overall effectiveness of fetal cardiac ultrasound screening in routine clinical workflows. To address these limitations and promote the automation of fetal cardiac ultrasound analysis，this study proposes FHSP-Net，a novel multitask deep learning model designed for accurate and comprehensive recognition of fetal heart standard planes. The model adopts a detection-then-classification strategy，which explicitly separates the anatomical structure detection task from the subsequent plane classification task，enabling effective feature extraction and decision-making for each subtask. This design reflects the clinical reasoning process in which a sonographer locates key anatomical structures and interprets them in the context of a specific standard plane.

In the detection task，FHSP-Net incorporates several carefully designed components to handle the inherent complexities of fetal ultrasound imaging. A path-weaved network （PWN） is developed to improve the multiscale representation of features，particularly enhancing the detection of small and morphologically variable cardiac structures. In addition，a target aware enhancer （TAE） module is introduced to strengthen object-level context modeling，making the detection branch highly sensitive to anatomical structures that are often obscured by noise，artifacts，or low contrast. Equalization loss function version 2 （EQLv2） is adopted to further mitigate the effect of data imbalance，which is common in clinical datasets where some structures or views are underrepresented. This loss formulation ensures that the model maintains stable learning dynamics across different categories and structure sizes，yielding robust and generalizable detection results. While the detection task ensures a strong foundation by locating relevant anatomical regions，the plane recognition task focuses on accurately classifying ultrasound views on the basis of the detected structures. Traditional algorithms that rely on whether certain anatomical structures are present in an image （i.e.，anatomical inclusion conditions） tend to have high precision but suffer from low recall. In other words，although they make only a few mistakes in their predictions，they often fail to identify all valid instances of each standard plane，resulting in missed diagnoses. To overcome this issue，we propose a candidate view scoring algorithm that evaluates and ranks multiple candidate views generated during the detection stage. This algorithm integrates structural cues and contextual features to refine classification decisions，substantially improving the model’s ability to capture valid plane instances across varied imaging conditions.

Experiments are conducted on a self-built fetal cardiac ultrasound dataset to evaluate the effectiveness of FHSP-Net. The dataset comprises a wide range of clinical scenarios and includes comprehensive annotations for anatomical structures and standard plane labels. Quantitative results demonstrate the superior performance of FHSP-Net across both subtasks. In the detection task，the model achieves a mean average precision of 0.962，representing a 0.027 improvement over a strong baseline model. This finding indicates that the proposed enhancements in feature fusion，object sensitivity，and loss balancing substantially benefit structure localization performance. In the plane recognition task，the candidate view scoring algorithm boosts classification accuracy by 0.10 compared with conventional structure-inclusion-based approaches. Ultimately，FHSP-Net attains an overall standard plane recognition accuracy of 0.959，representing a 0.194 improvement over the baseline model and the anatomical-inclusion-condition-based classification method. These results strongly validate the effectiveness and advantage of the proposed multitask design and its components.

The FHSP-Net multi-task model demonstrates strong capabilities in anatomical structure detection and fetal heart standard plane recognition. The integration of PWN，TAE，and EQLv2 enhances robustness and precision at the detection stage even under challenging imaging conditions. The candidate view scoring algorithm effectively addresses the recall bottleneck of previous classification methods，ensuring that the model captures a large portion of valid standard plane instances without sacrificing accuracy. Together，these innovations make FHSP-Net a reliable and efficient solution for intelligent ultrasound analysis. FHSP-Net has potential for advancing computer-aided diagnosis in prenatal cardiology.