Industrial park intelligent investment attraction management system and method based on machine learning

Abstract

The invention belongs to the technical field of business data processing, and particularly discloses an industrial park intelligent investment attraction management system and method based on machine learning, and the method comprises the steps: collecting an enterprise interaction log record, and extracting a feedback response index from the click rate and the stay duration; according to the index quantization professional depth and the initial weight of the data proportion, obtaining an attribute parameter vector; processing the attribute parameter vector by adopting a reinforcement learning algorithm, if a feedback response index exceeds a preset threshold value, improving the association strength of policy preference and case similarity, and obtaining an adjusted parameter vector through iterative updating; analyzing a restriction relationship between abstract description and emotional tendency according to the adjusted parameter vector, if the proportion of the abstract description is higher than a data proportion, reducing the proportion, and fusing chart response elements to determine an optimized content template; the invention aims to solve the problem of poor user interaction feedback and content attraction in the investment attracting content pushing process in the prior art.

Description

Technical Field

[0001] This invention relates to the technical field of business data processing, specifically to a smart investment promotion management system and method for industrial parks based on machine learning. Background Technology

[0002] Smart investment promotion management in industrial parks is a crucial area for driving regional economic development. It directly impacts the park's ability to attract high-quality enterprises and enhance its industrial agglomeration level, playing a key role in the current economic transformation and upgrading. As investment promotion activities increasingly rely on digital means, machine learning-based intelligent management systems have become the mainstream approach. These systems can provide precise content recommendations by analyzing enterprise needs, thereby improving investment promotion efficiency.

[0003] While existing investment promotion management systems have incorporated intelligent elements, they often struggle to continuously optimize content delivery. The main shortcomings lie in the lack of in-depth responsiveness to actual corporate feedback during parameter adjustments and the inability to effectively balance the complex relationships between various content attributes, making it difficult for the delivered content to maintain its appeal in the long term. Particularly in actual operation, while the materials pushed by the system may be effective initially, as corporate interaction increases, the content style struggles to flexibly adapt to the changing preferences of different companies.

[0004] The core technical challenge lies in the need for parametric modeling of various attributes of the investment promotion content. These attributes include quantifiable factors such as professional depth, policy incentives, case similarity, and textual sentiment. Because these attributes are interdependent—for example, increasing professional depth might decrease readability, while strengthening policy incentive descriptions might weaken the sense of relevance in case similarity—the system faces difficulties in dynamically adjusting parameters. If a company responds positively to detailed data and charts, and the system merely increases the data proportion without correspondingly reducing the proportion of abstract descriptions and balancing sentiment, the content will become too dry or lack persuasiveness, ultimately leading to a rapid decline in company attention.

[0005] Therefore, how to dynamically adjust the values ​​of multiple attribute parameters based on enterprise feedback during the process of pushing investment promotion content, while fully considering the mutual constraints between attributes, to ensure that the content can be both targeted and maintain overall attractiveness, has become a key issue in the research of intelligent investment promotion management methods and systems for industrial parks based on machine learning. Summary of the Invention

[0006] This invention provides a smart investment promotion management system and method for industrial parks based on machine learning, aiming to solve the problems of poor user interaction and content attractiveness in the existing technology during the process of pushing investment promotion content.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] The intelligent investment promotion management method for industrial parks based on machine learning includes: extracting feedback response indicators from click-through rates and dwell time by collecting enterprise interaction logs; obtaining an attribute parameter vector by quantifying the initial weights of professional depth and data proportion based on the indicators; processing the attribute parameter vector using a reinforcement learning algorithm; increasing the correlation strength between policy incentives and case similarity if the feedback response indicators exceed a preset threshold; obtaining an adjusted parameter vector through iterative updates; analyzing the constraint relationship between abstract descriptions and sentiment tendencies based on the adjusted parameter vectors; reducing the proportion of abstract descriptions if their weight is higher than the data proportion and integrating chart response elements to determine an optimized content template; obtaining preference change signals in the optimized content template and applying gradient descent to target the parameters... The process involves calculating the gradient difference between professional depth and policy incentives to obtain a balance constraint matrix. From this matrix, the matching degree of case similarity and sentiment is determined. If the matching degree is lower than a preset threshold, feedback response data is injected to reconstruct the matrix and determine the final attribute weight set. A targeted content push sequence is generated using this final attribute weight set, and an attractiveness balance index is obtained based on the data proportion and dynamic ratio of abstract descriptions within the sequence. The attractiveness balance index is used to verify the adaptability of the content style. If the style deviation is greater than zero, the parameter adjustment process is backtracked to update the feedback response model and determine a correction vector. Interaction items between policy incentives and chart responses are extracted from the correction vector, and the tracking path of preference changes is refined through these interaction items to obtain a continuously optimized push mechanism.

[0009] In one aspect of the invention, the step of extracting feedback response indicators from click-through rate and dwell time by collecting enterprise interaction log records, and obtaining an attribute parameter vector based on the initial weights of professional depth and data proportion according to the indicators, includes:

[0010] Raw record data is obtained by collecting enterprise interaction logs;

[0011] Click-through rate and dwell time are extracted from raw record data to obtain feedback response metrics;

[0012] The professional depth score and data proportion score are calculated based on the feedback response indicators to obtain a quantitative value;

[0013] A linear weighting method is used to assign initial weights to the quantized values ​​to obtain weighted scores.

[0014] If the professional depth score in the weighted score is higher than the data proportion score, then the weight ratio corresponding to professional depth will be increased to obtain the adjusted weight.

[0015] The attribute parameter vector is obtained by combining the professional depth and data proportion with the adjusted weights.

[0016] The overall strength value is obtained by calculating the vector magnitude based on the attribute parameter vector.

[0017] In one aspect of the invention, the step of using a reinforcement learning algorithm to process the attribute parameter vector, and increasing the correlation strength between policy incentives and case similarity if the feedback response index exceeds a preset threshold, and obtaining the adjusted parameter vector through iterative updates, includes:

[0018] The initial policy output is obtained by processing the attribute parameter vector using a reinforcement learning algorithm.

[0019] If the response indicator exceeds the preset threshold, the correlation strength between policy incentives and case similarity is enhanced to obtain an enhanced correlation matrix;

[0020] The initial adjustment vector is obtained by updating the corresponding components in the attribute parameter vector based on the enhanced correlation matrix;

[0021] The cumulative reward signal is obtained by multiplying the attribute parameter vector with the enhanced association matrix to determine the policy reward value;

[0022] The contribution ratio is obtained by determining the relative contribution of the professional depth component and the data proportion component in the attribute parameter vector based on the cumulative reward signal.

[0023] If the contribution ratio shows that the correlation strength of policy incentives is higher than that of case similarity, then the professional depth component weight is increased to obtain a secondary adjustment vector;

[0024] The optimized parameter vector is obtained by iteratively adjusting the input vector into the reinforcement learning algorithm to update the policy parameters.

[0025] In one aspect of the invention, the step of analyzing the constraint relationship between abstract description and sentiment tendency based on the adjusted parameter vector, and reducing the proportion of abstract description if its weight is higher than the data proportion, and integrating chart response elements to determine the optimized content template, includes:

[0026] Obtain the adjusted parameter vector, and use a pre-established analysis model to determine the constraint relationship between the abstract description and sentiment component contained therein.

[0027] Based on the constraints obtained from the analysis, it is determined whether the proportion of the abstract description in the parameter vector is higher than the proportion of the data. If it is higher than the preset threshold, the proportion adjustment mechanism is triggered to obtain the adjusted component distribution.

[0028] By adjusting the component distribution, the key elements in the chart response are obtained, and data mapping tools are used to associate the response elements with the abstract description components to determine the preliminary content framework.

[0029] Based on the initial content framework, the core information in the sentiment component is obtained, and through the information processing step, it is matched with the chart response elements to obtain the integrated content structure;

[0030] Based on the integrated content structure, the balance between emotional tendency and abstract description is judged. If the balance does not meet the preset standard, the optimized content template is determined by adjusting the presentation of response elements.

[0031] By optimizing the content template, the final parameter vector component distribution is obtained. A logic verification tool is used to determine its consistency with the initial target, and the final content output scheme is obtained.

[0032] Based on the final content output plan, the corresponding display logic is generated. Through the information processing stage, it is bound to the chart response elements to determine the complete business presentation format.

[0033] In one aspect of the invention, the step of obtaining the preference change signal in the optimized content template and calculating the gradient difference between professional depth and policy incentives using the gradient descent method to obtain the balance constraint matrix includes:

[0034] The signal of preference change is obtained, and the professional depth component and policy preference component are obtained through the information extraction process. The gradient difference between the two is calculated using a gradient calculation model to obtain a gradient difference sequence.

[0035] Based on the gradient difference sequence, determine the current proportion of the professional depth component. If the proportion is higher than the proportion of the policy incentive component, initiate the balance adjustment process and obtain the initial balance constraint value through proportional reduction.

[0036] By initially balancing the constraint values, the core preferential items in the policy preferential component are extracted. The data matching method is used to map the core preferential items one by one to the relevant content of the professional depth component, thus obtaining the basic content framework.

[0037] Based on the basic content framework, obtain the overall trend information of preference change signals, and extract the trend direction and intensity through the information extraction process to obtain a set of trend information;

[0038] By using trend information sets, the content positions of the basic content framework are adjusted, the presentation order of each part is determined, and the adjusted content structure is obtained.

[0039] Based on the adjusted content structure, the matching balance between the professional depth component and the policy incentive component is calculated. If the matching balance is lower than the preset standard, the presentation order of the core incentive items is changed by the order transformation method to obtain the optimized content template.

[0040] By using the optimized content template, the final presentation order and the correspondence between each component are extracted to determine the complete content output format.

[0041] In one aspect of the invention, the step of determining the matching degree of case similarity and sentiment tendency from the balance constraint matrix, and injecting feedback response data to reconstruct the matrix and determine the final attribute weight set if the matching degree is lower than a preset threshold, includes:

[0042] The case similarity component and sentiment component are extracted from the balance constraint matrix, and the matching degree is calculated to obtain the matching degree result.

[0043] If the matching result is lower than the preset threshold, the feedback response data is injected into the balance constraint matrix, and the matrix structure is reconstructed to obtain the intermediate weight matrix.

[0044] Highly relevant case items are identified by using an intermediate weight matrix, and a set of association weights is obtained by matching case similarity components and sentiment components using a data association method.

[0045] Calculate the weight distribution trend based on the associated weight set, and filter out the dominant attribute items to obtain the dominant weight subset;

[0046] The updated constraint matrix is ​​generated by modifying the row vector arrangement of the balance constraint matrix using the dominant weight subset.

[0047] The attribute weight balance values ​​are recalculated based on the updated constraint matrix to determine the final attribute weight set.

[0048] In one aspect of the invention, the step of generating a targeted content push sequence through a final attribute weight set, and obtaining an attractiveness balance index based on the dynamic ratio of data proportions and abstract descriptions in the sequence, includes:

[0049] The content items are weighted and sorted according to the final attribute weight set to generate an initial content push sequence;

[0050] The data percentage set is obtained by statistically analyzing the frequency of each type of content item in the initial content push sequence;

[0051] The abstract description distribution is obtained by mapping the final attribute weight set to the abstract description categories and then to each content item.

[0052] The dynamic proportion deviation is obtained by calculating the difference in the proportion of corresponding items based on the data proportion set and the abstract description distribution.

[0053] If the dynamic ratio deviation exceeds the preset threshold, the position of low-weight content items will be adjusted to obtain an optimized push sequence.

[0054] Based on the optimized push sequence, the frequency of content items and the distribution of abstract descriptions are re-statistically analyzed to obtain an updated data percentage set;

[0055] The attractiveness balance index is calculated by summing the updated data percentage set and the final attribute weight set.

[0056] In one aspect of the invention, the step of using the attractiveness balance index to verify the adaptability of the content style, and updating the feedback response model to determine the correction vector in the backtracking parameter adjustment step if the style deviation is greater than zero, includes:

[0057] The attractiveness balance index was used to assess content style adaptability and obtain style bias.

[0058] If the style deviation is greater than zero, the process will backtrack to the parameter adjustment stage to update the feedback response model.

[0059] Determine the correction vector from the updated feedback response model;

[0060] The adjusted parameter set is obtained by modifying the content of the vector and generating parameters.

[0061] Generate content style feature vectors based on the adjusted parameter set;

[0062] A style matching score is obtained by calculating the matching degree based on the content style feature vector and user preferences.

[0063] Content items are sorted by style matching score to form a revised push sequence;

[0064] The attractiveness balance index is recalculated by statistically analyzing the content distribution ratio in the revised push sequence and combining it with the revision vector.

[0065] In one aspect of the invention, the step of extracting the interaction items between policy incentives and chart responses from the correction vector, and refining the tracking path of preference changes through the interaction items to obtain a continuously optimized push mechanism, includes:

[0066] The interaction factors between policy incentives and chart responses are extracted from the correction vector, and the extracted interaction factors are classified to obtain a preliminary set of preference influences.

[0067] Based on the initial set of preference influences, we analyze the potential trends of preference changes and determine the path records of preference changes by constructing a data structure to track the routes.

[0068] Based on the path records of preference changes, a pre-established response pattern is used to dynamically update the output content of the push mechanism and obtain the adjusted content distribution strategy.

[0069] Based on the adjusted content distribution strategy and the user feedback data stream, if the feedback data indicates a shift in preferences, a continuous optimization process is triggered to determine the direction of optimization.

[0070] By optimizing the direction and updating the internal parameters of the push mechanism, a new content push sequence is obtained based on the dynamically updated parameter configuration.

[0071] Based on the new content push sequence, analyze the matching degree between interaction factors and response patterns, and determine the final content distribution scheme based on the matching degree results;

[0072] The final content distribution scheme is adopted, combined with continuously optimized data loops, to obtain real-time user feedback information and complete the response adjustment of the push mechanism.

[0073] In another aspect, the present invention also relates to a machine learning-based intelligent investment promotion management system for industrial parks, the system comprising:

[0074] The feedback response indicator extraction module is used to extract feedback response indicators from click-through rate and dwell time by collecting enterprise interaction log records, and obtain attribute parameter vectors based on the initial weights of professional depth and data proportion according to the indicators.

[0075] The reinforcement learning processing module is used to process the attribute parameter vector using a reinforcement learning algorithm. If the feedback response index exceeds a preset threshold, the correlation strength between policy incentives and case similarity is increased, and the adjusted parameter vector is obtained through iterative updates.

[0076] The optimized content template determination module is used to analyze the constraint relationship between abstract description and sentiment based on the adjusted parameter vector. If the proportion of abstract description is higher than the proportion of data, its proportion is reduced and chart response elements are integrated to determine the optimized content template.

[0077] The balance constraint matrix calculation module is used to obtain the preference change signal in the optimization content template, and calculate the gradient difference between professional depth and policy incentives through the gradient descent method for the parameter adjustment process to obtain the balance constraint matrix;

[0078] The final attribute weight set determination module is used to determine the matching degree of case similarity and sentiment tendency from the balance constraint matrix. If the matching degree is lower than the preset threshold, feedback response data is injected to reconstruct the matrix and determine the final attribute weight set.

[0079] The attractiveness balance index generation module is used to generate a targeted content push sequence through the final attribute weight set, and obtain the attractiveness balance index based on the data proportion and dynamic ratio of the abstract description in the sequence.

[0080] The feedback response model correction module is used to verify the adaptability of the content style using the attractiveness balance index. If the style deviation is greater than zero, the feedback response model is updated by backtracking the parameter adjustment step to determine the correction vector.

[0081] The continuous optimization push mechanism generation module is used to extract the interaction items between policy incentives and chart responses from the correction vector, and to refine the tracking path of preference changes through the interaction items to obtain the continuous optimization push mechanism.

[0082] Compared with the prior art, the present invention has the following beneficial effects:

[0083] This invention extracts feedback response metrics from click-through rates and dwell time by collecting enterprise interaction logs. It quantifies initial weights for attributes such as professional depth, data proportion, policy incentives, and case similarity to form a parameter vector. A reinforcement learning algorithm iteratively processes this vector, increasing the correlation strength between policy incentives and case similarity when feedback metrics exceed a threshold, thus obtaining an adjusted parameter vector. Based on this, it analyzes the constraint relationship between abstract description and sentiment. If the proportion of abstract description is higher than the data proportion, its proportion is reduced, and chart elements are integrated to optimize the content template. Furthermore, gradient descent is used to calculate the gradient difference between professional depth and policy incentives to construct a balance constraint matrix, determining the matching degree between case similarity and sentiment. If it is below a threshold, feedback data is injected to reconstruct the matrix and determine the final weight set. Finally, a targeted push sequence is generated, and an attractiveness balance index is calculated to verify style adaptability. If deviations exist, the model is backtracked and updated to extract interaction items and refine preference tracking, achieving continuous optimization of the push mechanism. The core of this invention lies in combining reinforcement learning and gradient optimization to dynamically balance the data-driven and emotional appeal of content, ensuring that the pushed content achieves the optimal balance between professionalism, policy relevance, and user preferences, thereby improving user interaction feedback and content attractiveness. Attached Figure Description

[0084] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0085] Figure 1 This is a flowchart of a machine learning-based intelligent investment promotion management method for industrial parks according to the present invention. Detailed Implementation

[0086] The present invention will be further described below with reference to embodiments. These embodiments are merely some, not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the protection scope of the present invention.

[0087] Please see Figure 1 As shown in the figure, this embodiment discloses a smart investment promotion management method for industrial parks based on machine learning, which may specifically include:

[0088] Step 101: Extract feedback response metrics from click-through rate and dwell time by collecting enterprise interaction log records, and obtain attribute parameter vectors by quantifying the initial weights of professional depth and data proportion based on the metrics.

[0089] Raw data is obtained by collecting enterprise interaction logs. Click-through rate and dwell time are extracted from the raw data to obtain feedback response metrics. Based on the feedback response metrics, a professional depth score and a data share score are calculated to obtain quantified values. An initial weight is assigned to the quantified values ​​using a linear weighting method to obtain a weighted score. If the professional depth score is higher than the data share score in the weighted score, the weight ratio corresponding to professional depth is increased to obtain an adjusted weight. The professional depth and data share are combined using the adjusted weight to obtain an attribute parameter vector. The vector magnitude is calculated based on the attribute parameter vector to obtain the comprehensive strength value.

[0090] First, front-end tracking scripts are deployed to collect user interaction logs on the platform, including page click events and element dwell time. For example, JavaScript event listeners are used to record the element ID, timestamp, and page URL for each click. Simultaneously, the IntersectionObserver API is used to monitor element exposure time. The collection cycle is daily incremental logs imported into Hadoop distributed storage, with approximately 5 million log entries per day. Second, feedback response metrics are extracted from the raw logs. Specifically, the click-through rate (CTR) is calculated as the number of clicks for a specific functional module divided by the number of exposures. For example, if a report generation module has 10,000 exposures and 3,200 clicks, then the CTR = 0.32. Dwell time is taken as the average value; for example, the average dwell time for this module is 45.6 seconds. Then, the CTR and dwell time are normalized. The Min-Max normalization formula maps the CTR to the [0,1] interval, resulting in 0.64. The average dwell time is divided by a preset maximum threshold of 300 seconds, and then min(1,45.6 / 300) = 0.152. Subsequently, the initial weights of professional depth and data proportion were quantified based on these indicators. The weight of professional depth was set to be positively correlated with normalized dwell time and negatively correlated with click-through rate (CTR) (because a high CTR may indicate shallow operation). A linear combination algorithm was adopted: Initial weight of professional depth = 0.7 × normalized dwell time + 0.3 × (1 - normalized CTR) = 0.7 × 0.152 + 0.3 × (1 - 0.64) = 0.1064 + 0.108 = 0.2144; The weight of data proportion was positively correlated with CTR and adjusted by dwell time. The algorithm was: Initial weight of data proportion = 0.6 × normalized CTR + 0.4 × normalized dwell time = 0.6 × 0.64 + 0.4 × 0.152 = 0.384 + 0.0608 = 0.4448. Finally, the two weights are combined into an attribute parameter vector [0.2144, 0.4448], and L2 normalized to obtain the final vector [0.433, 0.899]. This vector can be directly input into subsequent recommendation models or content ranking algorithms to achieve adaptive weight adjustment based on implicit feedback of user behavior. The entire process is automated through Spark Streaming real-time pipeline without manual intervention.

[0091] Step 102: The attribute parameter vector is processed using a reinforcement learning algorithm. If the feedback response index exceeds a preset threshold, the correlation strength between policy incentives and case similarity is increased. The adjusted parameter vector is obtained through iterative updates.

[0092] An initial policy output is obtained by processing the attribute parameter vector using a reinforcement learning algorithm. If the response index exceeds a preset threshold, the correlation strength between policy incentives and case similarity is increased to obtain an enhanced correlation matrix. The corresponding components in the attribute parameter vector are updated based on the enhanced correlation matrix to obtain a preliminary adjustment vector. The policy reward value is determined by multiplying the attribute parameter vector with the enhanced correlation matrix to obtain a cumulative reward signal. The relative contribution of the professional depth component and the data proportion component in the attribute parameter vector is determined based on the cumulative reward signal to obtain the contribution ratio. If the contribution ratio shows that the correlation strength of policy incentives is higher than that of case similarity, the weight of the professional depth component is increased to obtain a secondary adjustment vector. The optimized parameter vector is obtained by iteratively inputting the secondary adjustment vector into the reinforcement learning algorithm to update the policy parameters.

[0093] Using the attribute parameter vector [0.433, 0.899] obtained in the previous stage as the state input, the Q-learning reinforcement learning algorithm is used for policy optimization. The environment reward function is designed as a comprehensive feedback score, that is, if the normalized click-through rate exceeds the 0.7 threshold, a reward of +1.5 is given; if the average dwell time exceeds 120 seconds, an additional reward of +0.8 is given; otherwise, a penalty of -0.5 is given. For example, if the actual observed click-through rate of a new batch of logs in the current state is 0.75 and the dwell time is 135 seconds, the reward is calculated as 1.5 + 0.8 = 2.3. The action space is defined as the adjustment range of the weights of professional depth and data proportion, discretized into five actions [-0.1, -0.05, 0, 0.05, 0.1], corresponding to the synchronous increase or decrease of two components of the vector. When selecting an action, an ε-greedy strategy is adopted, with ε initially set to 0.3 and decreasing to 0.01 with each iteration. Since the random number in this iteration is less than ε, the action selection is increased by 0.05, resulting in a new candidate vector [0.483, 0.949], which, after L2 normalization, becomes [0.453, 0.891]. Forward simulation is performed to enhance the association strength between the policy incentive module and the case library under this new vector. The cosine similarity increases from 0.82 to 0.89. After confirming a positive reward, a Q-value update is performed: Q(s,a) = Q(s,a) + α[r + γmaxQ(s',a') - Q(s,a)], where the learning rate α = 0.15 and the discount factor γ = 0.9. After the update, the Q-value increases from 1.82 to 2.14. Meanwhile, cosine similarity weighting was introduced for case similarity calculation. When the proportion of data is higher than 0.85, the recall weight of similar cases is automatically increased by 20%. In this case, 0.891 > 0.85, so the increase was triggered. In the actual recall of Top 10 cases, the average matching degree increased from 0.76 to 0.88. The entire iterative process was executed in batches once per hour by a TensorFlow distributed training cluster. After a total of 1000 iterations, the adjusted parameter vector [0.512, 0.859] was obtained, realizing feedback-driven dynamic optimization.

[0094] Step 103: Analyze the constraint relationship between abstract description and sentiment based on the adjusted parameter vector. If the proportion of abstract description is higher than the proportion of data, reduce its proportion and integrate chart response elements to determine the optimized content template.

[0095] The process begins by obtaining the adjusted parameter vector. For the abstract description and sentiment components it contains, a pre-established analysis model is used to determine the constraints between them. Based on these constraints, the proportion of abstract description in the parameter vector is checked against the data proportion. If it exceeds a preset threshold, a weighting adjustment mechanism is triggered, resulting in an adjusted component distribution. Using this adjusted distribution, key elements in the chart response are identified. A data mapping tool is used to associate these response elements with the abstract description components, establishing a preliminary content framework. For this preliminary framework, core information from the sentiment component is extracted and matched with the chart response elements through information processing, resulting in a fused content structure. Based on this fused content structure, the balance between sentiment and abstract description is assessed. If the balance does not meet a preset standard, the presentation of the response elements is adjusted to determine an optimized content template. Using this optimized content template, the final parameter vector component distribution is obtained. A logic verification tool is used to check its consistency with the initial target, resulting in the final content output scheme. Based on this final content output scheme, corresponding display logic is generated and bound to the chart response elements through information processing, determining the complete business presentation format.

[0096] Based on the adjusted parameter vector analysis, the constraint relationship between abstract description and sentiment tendency is analyzed. Assuming the current parameter vector is [0.621, 0.784], where the first component represents the weight of the abstract description and the second component represents the weight of the data proportion, a comparison reveals that the weight of the abstract description (0.621) is lower than the weight of the data proportion (0.784), failing to reach the expected balance of 0.65. Therefore, the system automatically triggers an adjustment mechanism, aiming to reduce the abstract description proportion to 0.58, while simultaneously introducing chart-based responsive elements to enhance content expressiveness. First, the system uses a linear interpolation algorithm to calculate the adjustment magnitude, setting the target vector to [0.58, 0.815]. By calculating the Euclidean distance between the two vectors to be 0.045, the adjustment step size is determined to be 0.015, gradually decreasing the weight of the abstract description by 0.005 in each iteration. After three iterations, the target value of 0.58 is reached. Secondly, regarding the constraint analysis of sentiment tendency, the system calls the sentiment classification model. Inputting the current content sample, it outputs a sentiment tendency score of 0.72 (positive sentiment percentage). If the score is below the threshold of 0.75, it automatically integrates a sentiment enhancement vocabulary, extracting high-frequency positive words and embedding them into the content template, improving the vocabulary matching accuracy from 0.68 to 0.76. Next, to integrate responsive chart elements, the system automatically generates a dynamic chart template based on a data percentage weight of 0.815, setting the chart percentage to 25% of the total content. By analyzing user behavior logs, the chart type is determined to be a bar chart, with a data point density of 10 per unit to ensure clear visual presentation. Finally, the system matches the optimized content template with a user preference database based on business needs. If the matching accuracy is below 0.8, it automatically adjusts the chart color scheme, switching from the default blue to a green scheme to enhance visual appeal, improving the matching accuracy from 0.77 to 0.83. The entire process is executed every 4 hours by an automated script, ensuring continuous optimization of the content template and logically forming a complete chain from parameter adjustment to sentiment enhancement to visual presentation.

[0097] Step 104: Obtain the preference change signal in the optimized content template, and calculate the gradient difference between professional depth and policy incentives for the parameter adjustment process using the gradient descent method to obtain the balance constraint matrix.

[0098] The process involves acquiring preference change signals, extracting the professional depth component and the policy incentive component through information extraction, and calculating the gradient difference between them using a gradient calculation model to obtain a gradient difference sequence. Based on this sequence, the current proportion of the professional depth component is determined. If its proportion is higher than that of the policy incentive component, a balancing adjustment process is initiated, using a proportional reduction operation to obtain an initial balancing constraint value. Using this initial constraint value, core incentive items within the policy incentive component are extracted, and a data matching method is used to map each core incentive item to relevant content within the professional depth component, resulting in a basic content framework. Based on this framework, the overall trend information of the preference change signal is acquired, and the trend direction and intensity are extracted through information extraction to obtain a trend information set. Using this trend information set, the content positions within the basic content framework are adjusted to determine the presentation order of each part, resulting in an adjusted content structure. Based on this adjusted structure, the matching balance between the professional depth component and the policy incentive component is calculated. If the matching balance is lower than a preset standard, a sequence transformation method is used to change the presentation order of the core incentive items, resulting in an optimized content template. Using this optimized template, the final presentation order and its correspondence with each component are extracted to determine the complete content output format.

[0099] The system first extracts preference change signals from user interaction logs, calculating that the professional depth preference score increased from 0.45 to 0.62 and the policy preference score decreased from 0.71 to 0.58 in the past 30 days, forming a preference vector difference [0.17, -0.13], which is used as the initial gradient input. Next, the gradient descent algorithm is used to optimize the parameter adjustment process, setting the learning rate to 0.02 and the loss function to the mean squared error of the weights of professional depth and policy preferences. The initial parameter matrix is ​​[[0.55, 0.45], [0.40, 0.60]]. By calculating the gradient difference ∂L / ∂W, [[0.08, -0.08], [-0.06, 0.06]] is obtained. After one iteration update, the matrix is ​​adjusted to [[0.5384, 0.4616], [0.4012, 0.5988]], and the loss value decreases from 0.032 to 0.018. The system further constructs a balance constraint matrix, compares the updated parameters with the preset balance threshold of 0.5, and finds that the deviation of the professional depth column is 0.0384, exceeding the tolerance of 0.03. Therefore, an L2 regularization term λ=0.01 is introduced to recalculate the gradient. After the second iteration, the matrix converges to [[0.542,0.458], [0.399,0.601]], achieving a hard constraint that the sum of the weights of the two dimensions is 1.0. Subsequently, an optimization signal is generated based on this matrix, automatically increasing the proportion of professional depth descriptions in the content template to 54.2% and adjusting the proportion of policy incentive descriptions to 45.8%. The matching degree with the user preference vector is improved from 0.74 to 0.89 through cosine similarity verification. The entire optimization process is triggered by a scheduled task every 6 hours, forming a closed-loop automated chain from preference signal capture to gradient optimization and then to constraint matrix application.

[0100] Step 105: Determine the matching degree of case similarity and sentiment from the balance constraint matrix. If the matching degree is lower than the preset threshold, inject feedback response data to reconstruct the matrix and determine the final attribute weight set.

[0101] The case similarity component and sentiment component are extracted from the balanced constraint matrix, and their matching degree is calculated to obtain the matching result. If the matching result is lower than a preset threshold, feedback response data is injected into the balanced constraint matrix to reconstruct the matrix structure and obtain an intermediate weight matrix. Highly relevant case items are identified using the intermediate weight matrix, and the case similarity component and sentiment component are matched using a data association method to obtain an associated weight set. The weight distribution trend is calculated based on the associated weight set, and dominant attribute items are selected to obtain a dominant weight subset. The row vector arrangement of the balanced constraint matrix is ​​modified using the dominant weight subset to generate an updated constraint matrix. The attribute weight equilibrium value is recalculated based on the updated constraint matrix to determine the final attribute weight set.

[0102] The system first extracts a balance constraint matrix from the historical case database and analyzes the matching degree of case similarity and sentiment. The initial matrix is ​​assumed to be [[0.6, 0.4], [0.3, 0.7]], where the row vectors represent the weight distribution of case content and sentiment expression, respectively. By calculating the Euclidean distance between the case feature vector and the target template, a similarity score of 0.65 is obtained. Simultaneously, the sentiment score calculated using the text sentiment analysis model is 0.58, both lower than the preset threshold of 0.7, triggering a feedback response mechanism. Next, the system automatically injects user feedback data from the past 7 days, containing 1000 sentiment-annotated records, with positive sentiment accounting for 0.62%, neutral for 0.28%, and negative for 0.1%. The matrix is ​​reconstructed using a weighted average algorithm, resulting in the adjusted matrix [[0.58, 0.42], [0.35, 0.65]], and the softmax function ensures that the sum of the weights in each row is 1. Subsequently, the system further optimizes the attribute weight set using the least squares method, setting the objective function as the weighted sum of errors of case similarity and sentiment tendency, with weight ratios of 0.6 and 0.4, respectively. The final attribute weight set is calculated to be [0.55, 0.45], ensuring a balance between the two. To form a closed-loop logic, the system associates the weight set with the content recommendation module, automatically adjusting the proportion of case descriptions and sentiment tone in the recommended content. The proportion of case descriptions is increased to 0.57, and the proportion of sentiment tone is decreased to 0.43. The matrix data is updated every 12 hours through a scheduled task to ensure continuous adaptation to changes in user needs. The entire process is automated analysis and adjustment driven by algorithms.

[0103] Step 106: Generate a targeted content push sequence through the final attribute weight set, and obtain an attractiveness balance index based on the data proportion and dynamic ratio of abstract description in the sequence.

[0104] An initial content push sequence is generated by weighting and sorting content items based on the final attribute weight set. The frequency percentage of each content item type is then calculated from this initial sequence to obtain a data percentage set. An abstract description distribution is obtained by mapping abstract description categories to each content item using the final attribute weight set. The dynamic proportion deviation is calculated by comparing the proportions of corresponding items with the data percentage set and the abstract description distribution. If the dynamic proportion deviation exceeds a preset threshold, the positions of low-weight content items are adjusted to obtain an optimized push sequence. An updated data percentage set is then obtained by recalculating the frequency percentage of content items and the abstract description distribution based on the optimized push sequence. Finally, an attractiveness balance index is calculated by multiplying the updated data percentage set by the final attribute weight set and summing the results.

[0105] The system generates a targeted content push sequence based on the final attribute weight set [0.55, 0.45]. First, the weight set is mapped to the content abstraction layer, with 0.55 allocated to factual descriptions and 0.45 to emotional rendering descriptions. A vector embedding model transforms each piece of content in the candidate content library into a 512-dimensional feature vector, calculating the cosine similarity with the user's interest template vector. The top 200 highly similar content pieces are selected to form the initial sequence. Next, an attractiveness balance index is calculated based on the dynamic ratio. The index formula is defined as the mean similarity multiplied by 0.6 plus the emotional resonance score multiplied by 0.4, where the emotional resonance score is output by a BERT-based sentiment classifier. Processing the user's historical interaction data for each piece of content in the sequence yields an average similarity of 0.72 and an average emotional resonance of 0.68. Substituting these values ​​into the formula, the attractiveness balance index is calculated to be 0.704. If the index falls below the preset threshold of 0.75, a sequence rearrangement mechanism is triggered. The PPO algorithm in reinforcement learning is used to fine-tune the sequence position. The reward function is set as a weighted sum of the predicted click-through rate and the diversity penalty, with weights of 0.7 and 0.3, respectively. After 50 iterations of optimization, the proportion of case fact descriptions in the first 10 items of the sequence increases to 0.59, the proportion of emotional rendering decreases to 0.41, and the attractiveness balance index rises to 0.778. The system further monitors the actual click-through rate and dwell time after the push in real time through the A / B testing module, collects 5,000 exposure data in the last 24 hours, with a positive interaction ratio of 0.67, calculates the actual attractiveness gain as 0.082, and feeds this gain back to the weight set fine-tuning layer. The gradient descent method is used to update the attribute weight set to [0.56, 0.44] with a learning rate of 0.001, realizing closed-loop adaptive optimization of the push sequence. The entire process is automatically scheduled by the distributed computing framework to ensure that the content attractiveness remains at a high level of balance.

[0106] Step 107: Use the attraction balance index to verify the adaptability of the content style. If the style deviation is greater than zero, backtrack the parameter adjustment step to update the feedback response model and determine the correction vector.

[0107] The attractiveness balance index is used to assess content style adaptability and identify style deviation. If the style deviation is greater than zero, the process backtracks to the parameter adjustment stage to update the feedback response model. A correction vector is determined from the updated feedback response model. The content generation parameters are adjusted using the correction vector to obtain an adjusted parameter set. A content style feature vector is generated based on the adjusted parameter set. The matching degree between the content style feature vector and user preferences is calculated to obtain a style matching score. Content items are sorted based on the style matching score to form a corrected push sequence. The content distribution ratio is statistically analyzed from the corrected push sequence, and the attractiveness balance index is recalculated in conjunction with the correction vector.

[0108] In the field of content recommendation, to verify the adaptability of content styles and handle style deviations, the system first quantitatively evaluates content styles using an attractiveness balance index. Assuming the current index value is 0.71 and the preset style adaptability threshold is 0.80, the calculated style deviation is 0.09, indicating a certain deviation that needs adjustment. The system automatically triggers a backtracking parameter adjustment step, extracting the most recent 10,000 user feedback records from historical push data. Analyzing the content style distribution using a natural language processing model reveals that logical descriptions account for 0.62%, while engaging descriptions account for only 0.38%, deviating from the ideal equilibrium ratio of 0.5:0.5. Next, the system calls the feedback response model, and based on the deviation value of 0.09 and the style distribution data, uses a support vector machine algorithm to calculate a correction vector. The kernel function is set to a radial basis function, and the iteration count is 30, ultimately generating a correction vector [0.12, -0.12] to increase the weight of engaging descriptions. The model further applies the correction vector to the content generation module, adjusting content style parameters to reduce the proportion of logical descriptions to 0.55 and increase the proportion of engaging descriptions to 0.45. Simultaneously, by analyzing user browsing preference data from the past 7 days, the model verifies that the adjusted style distribution improved the match with user interests by 0.07. The system then updates the parameter matrix of the feedback response model, optimizing it using a gradient descent algorithm with a learning rate of 0.002 to ensure more accurate subsequent style deviation detection. The entire process is completed in a cloud computing cluster via automated scripts, forming a closed-loop logic from deviation detection to parameter correction. To further enhance system adaptability, a content theme diversity assessment module is introduced into related business processes, increasing the calculated theme coverage from 0.65 to 0.73. This ensures that style adjustment takes into account content breadth, constructing a collaborative optimization mechanism between style and theme.

[0109] Step 108: Extract the interaction items between policy incentives and chart responses from the correction vector, and refine the tracking path of preference changes through the interaction items to obtain a continuously optimized push mechanism.

[0110] Interaction factors between policy incentives and chart responses are extracted from the correction vector. These extracted interaction factors are then categorized to obtain a preliminary set of preference influences. Based on this preliminary set, potential trends in preference changes are analyzed, and path records of preference changes are determined by constructing a data structure to track these paths. According to these path records, the output content of the push mechanism is dynamically updated using a pre-established response pattern to obtain an adjusted content distribution strategy. For the adjusted content distribution strategy, combined with user feedback data, if feedback data indicates a preference shift, a continuous optimization process is triggered to determine the optimization direction. Based on this optimization direction, the internal parameters of the push mechanism are updated, and a new content push sequence is obtained based on the dynamically updated parameter configuration. Based on the new content push sequence, the matching degree between interaction factors and response patterns is analyzed, and the final content distribution scheme is determined based on the matching degree results. Using the final content distribution scheme, combined with the continuously optimized data loop, real-time user feedback information is obtained to complete the response adjustment of the push mechanism.

[0111] In the field of content recommendation, to optimize the push mechanism, the system first extracts the interaction items between policy incentives and chart responses from the correction vector. Specifically, by analyzing the most recent 5000 user interaction data, the click-through rate (CTR) of policy incentive content is calculated to be 0.45%, while that of chart responses is 0.35%. Combining this with the correction vector [0.15, -0.10], a linear regression algorithm is used to determine the influence weight of the interaction item as 0.25, indicating that the interaction between the two has a significant effect on changes in user preferences. Next, the system refines the tracking path of preference changes based on this interaction item. Using a time series analysis model, the user behavior data of the past 14 days is broken down by hourly granularity to calculate the preference change rate. It is found that the preference growth rate of policy incentive-related content is 0.08, while the growth rate of chart responses is only 0.03. This generates a preference change curve, predicting a preference trend shift of approximately 0.05 for the next 3 days. Subsequently, the system continuously optimized its push mechanism, matching preference change tracking paths with the push strategy database. It then used a random forest algorithm to re-prioritize push content, setting the feature dimensions to 20 and iterations to 50. Ultimately, the push frequency of policy incentive content was increased from twice daily to three times daily, while the display ratio of chart responses was increased from 0.30% to 0.40%. To ensure push effectiveness, the system automatically linked to the user profile module, extracting user age distribution data. It was found that users aged 25-35 had a preference for policy incentive content of 0.60. Based on this, the push time window was further fine-tuned, focusing on the evening period from 19:00 to 21:00 when user activity is highest. The entire process was automatically executed in the cloud through a distributed computing framework, forming a complete closed loop from interaction item extraction to push optimization.

[0112] This invention also provides a machine learning-based intelligent investment promotion management system for industrial parks, mainly comprising:

[0113] The feedback response indicator extraction module is used to extract feedback response indicators from click-through rate and dwell time by collecting enterprise interaction log records, and obtain attribute parameter vectors based on the initial weights of professional depth and data proportion according to the indicators.

[0114] The reinforcement learning processing module is used to process the attribute parameter vector using a reinforcement learning algorithm. If the feedback response index exceeds a preset threshold, the correlation strength between policy incentives and case similarity is increased, and the adjusted parameter vector is obtained through iterative updates.

[0115] The optimized content template determination module is used to analyze the constraint relationship between abstract description and sentiment based on the adjusted parameter vector. If the proportion of abstract description is higher than the proportion of data, its proportion is reduced and chart response elements are integrated to determine the optimized content template.

[0116] The balance constraint matrix calculation module is used to obtain the preference change signal in the optimization content template, and calculate the gradient difference between professional depth and policy incentives through the gradient descent method for the parameter adjustment process to obtain the balance constraint matrix;

[0117] The final attribute weight set determination module is used to determine the matching degree of case similarity and sentiment tendency from the balance constraint matrix. If the matching degree is lower than the preset threshold, feedback response data is injected to reconstruct the matrix and determine the final attribute weight set.

[0118] The attractiveness balance index generation module is used to generate a targeted content push sequence through the final attribute weight set, and obtain the attractiveness balance index based on the data proportion and dynamic ratio of the abstract description in the sequence.

[0119] The feedback response model correction module is used to verify the adaptability of the content style using the attractiveness balance index. If the style deviation is greater than zero, the feedback response model is updated by backtracking the parameter adjustment step to determine the correction vector.

[0120] The continuous optimization push mechanism generation module is used to extract the interaction items between policy incentives and chart responses from the correction vector, and to refine the tracking path of preference changes through the interaction items to obtain the continuous optimization push mechanism.

[0121] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A machine learning-based intelligent investment promotion management method for industrial parks, characterized in that: include: By collecting enterprise interaction logs, feedback response indicators are extracted from click-through rate and dwell time. Based on these indicators, the initial weights of professional depth and data proportion are quantified to obtain an attribute parameter vector. The attribute parameter vector is processed using a reinforcement learning algorithm. If the feedback response index exceeds a preset threshold, the correlation strength between policy incentives and case similarity is increased. The adjusted parameter vector is obtained through iterative updates. Based on the adjusted parameter vector analysis, the relationship between abstract description and sentiment is constrained. If the proportion of abstract description is higher than the proportion of data, its proportion is reduced and chart response elements are integrated to determine the optimized content template. Obtain the preference change signal in the optimized content template, and calculate the gradient difference between professional depth and policy incentives through the gradient descent method to obtain the balance constraint matrix. The matching degree of case similarity and sentiment tendency is determined from the balance constraint matrix. If the matching degree is lower than the preset threshold, feedback response data is injected to reconstruct the matrix and determine the final attribute weight set. A targeted content push sequence is generated by the final attribute weight set, and an attractiveness balance index is obtained based on the proportion of data in the sequence and the dynamic ratio of abstract description. The attractiveness balance index is used to verify the adaptability of the content style. If the style deviation is greater than zero, the parameter adjustment process is backtracked to update the feedback response model and determine the correction vector. The interaction items between policy incentives and chart responses are extracted from the correction vector, and the tracking path of preference changes is refined through these interaction items to obtain a continuously optimized push mechanism.

2. The intelligent investment promotion management method for industrial parks based on machine learning according to claim 1, characterized in that, The process involves extracting feedback response metrics from click-through rate and dwell time by collecting enterprise interaction logs. Based on these metrics, an initial weighting of professional depth and data proportion is used to obtain an attribute parameter vector, including: Raw record data is obtained by collecting enterprise interaction logs; Click-through rate and dwell time are extracted from raw record data to obtain feedback response metrics; The professional depth score and data proportion score are calculated based on the feedback response indicators to obtain a quantitative value; A linear weighting method is used to assign initial weights to the quantized values ​​to obtain weighted scores. If the professional depth score in the weighted score is higher than the data proportion score, then the weight ratio corresponding to professional depth will be increased to obtain the adjusted weight. The attribute parameter vector is obtained by combining the professional depth and data proportion with the adjusted weights. The overall strength value is obtained by calculating the vector magnitude based on the attribute parameter vector.

3. The intelligent investment promotion management method for industrial parks based on machine learning according to claim 1, characterized in that, The reinforcement learning algorithm is used to process the attribute parameter vector. If the feedback response index exceeds a preset threshold, the correlation strength between policy incentives and case similarity is increased. The adjusted parameter vector is obtained through iterative updates, including: The initial policy output is obtained by processing the attribute parameter vector using a reinforcement learning algorithm. If the response indicator exceeds the preset threshold, the correlation strength between policy incentives and case similarity is enhanced to obtain an enhanced correlation matrix; The initial adjustment vector is obtained by updating the corresponding components in the attribute parameter vector based on the enhanced correlation matrix; The cumulative reward signal is obtained by multiplying the attribute parameter vector with the enhanced association matrix to determine the policy reward value; The contribution ratio is obtained by determining the relative contribution of the professional depth component and the data proportion component in the attribute parameter vector based on the cumulative reward signal. If the contribution ratio shows that the correlation strength of policy incentives is higher than that of case similarity, then the professional depth component weight is increased to obtain a secondary adjustment vector; The optimized parameter vector is obtained by iteratively adjusting the input vector into the reinforcement learning algorithm to update the policy parameters.

4. The intelligent investment promotion management method for industrial parks based on machine learning according to claim 1, characterized in that: The process involves analyzing the constraint relationship between abstract descriptions and sentiment tendencies based on the adjusted parameter vectors. If the weight of abstract descriptions is higher than the data percentage, their proportion is reduced, and chart response elements are integrated to determine the optimized content template, including: Obtain the adjusted parameter vector, and use a pre-established analysis model to determine the constraint relationship between the abstract description and sentiment component contained therein. Based on the constraints obtained from the analysis, it is determined whether the proportion of the abstract description in the parameter vector is higher than the proportion of the data. If it is higher than the preset threshold, the proportion adjustment mechanism is triggered to obtain the adjusted component distribution. By adjusting the component distribution, the key elements in the chart response are obtained, and data mapping tools are used to associate the response elements with the abstract description components to determine the preliminary content framework. Based on the initial content framework, the core information in the sentiment component is obtained, and through the information processing step, it is matched with the chart response elements to obtain the integrated content structure; Based on the integrated content structure, the balance between emotional tendency and abstract description is judged. If the balance does not meet the preset standard, the optimized content template is determined by adjusting the presentation of response elements. By optimizing the content template, the final parameter vector component distribution is obtained. A logic verification tool is used to determine its consistency with the initial target, and the final content output scheme is obtained. Based on the final content output plan, the corresponding display logic is generated. Through the information processing stage, it is bound to the chart response elements to determine the complete business presentation format.

5. The intelligent investment promotion management method for industrial parks based on machine learning according to claim 1, characterized in that: The process of obtaining preference change signals from the optimized content template, and calculating the gradient difference between professional depth and policy incentives using the gradient descent method to obtain the balance constraint matrix during the parameter adjustment process, includes: The signal of preference change is obtained, and the professional depth component and policy preference component are obtained through the information extraction process. The gradient difference between the two is calculated using a gradient calculation model to obtain a gradient difference sequence. Based on the gradient difference sequence, determine the current proportion of the professional depth component. If the proportion is higher than the proportion of the policy incentive component, initiate the balance adjustment process and obtain the initial balance constraint value through proportional reduction. By initially balancing the constraint values, the core preferential items in the policy preferential component are extracted. The data matching method is used to map the core preferential items one by one to the relevant content of the professional depth component, thus obtaining the basic content framework. Based on the basic content framework, obtain the overall trend information of preference change signals, and extract the trend direction and intensity through the information extraction process to obtain a set of trend information; By using trend information sets, the content positions of the basic content framework are adjusted, the presentation order of each part is determined, and the adjusted content structure is obtained. Based on the adjusted content structure, the matching balance between the professional depth component and the policy incentive component is calculated. If the matching balance is lower than the preset standard, the presentation order of the core incentive items is changed by the order transformation method to obtain the optimized content template. By using the optimized content template, the final presentation order and the correspondence between each component are extracted to determine the complete content output format.

6. The intelligent investment promotion management method for industrial parks based on machine learning according to claim 1, characterized in that: The process of determining the matching degree of case similarity and sentiment tendency from the balance constraint matrix, and injecting feedback response data to reconstruct the matrix and determine the final attribute weight set if the matching degree is lower than a preset threshold, includes: The case similarity component and sentiment component are extracted from the balance constraint matrix, and the matching degree is calculated to obtain the matching degree result. If the matching result is lower than the preset threshold, the feedback response data is injected into the balance constraint matrix, and the matrix structure is reconstructed to obtain the intermediate weight matrix. Highly relevant case items are identified by using an intermediate weight matrix, and a set of association weights is obtained by matching case similarity components and sentiment components using a data association method. Calculate the weight distribution trend based on the associated weight set, and filter out the dominant attribute items to obtain the dominant weight subset; The updated constraint matrix is ​​generated by modifying the row vector arrangement of the balance constraint matrix using the dominant weight subset. The attribute weight balance values ​​are recalculated based on the updated constraint matrix to determine the final attribute weight set.

7. The intelligent investment promotion management method for industrial parks based on machine learning according to claim 1, characterized in that: The process of generating a targeted content push sequence through the final attribute weight set, and obtaining an attractiveness balance index based on the dynamic proportion of data and abstract descriptions in the sequence, includes: The content items are weighted and sorted according to the final attribute weight set to generate an initial content push sequence; The data percentage set is obtained by statistically analyzing the frequency of each type of content item in the initial content push sequence; The abstract description distribution is obtained by mapping the final attribute weight set to the abstract description categories and then to each content item. The dynamic proportion deviation is obtained by calculating the difference in the proportion of corresponding items based on the data proportion set and the abstract description distribution. If the dynamic ratio deviation exceeds the preset threshold, the position of low-weight content items will be adjusted to obtain an optimized push sequence. Based on the optimized push sequence, the frequency of content items and the distribution of abstract descriptions are re-statistically analyzed to obtain an updated data percentage set; The attractiveness balance index is calculated by summing the updated data percentage set and the final attribute weight set.

8. The intelligent investment promotion management method for industrial parks based on machine learning according to claim 1, characterized in that: The process of using the attractiveness balance index to verify the adaptability of content style, and if the style deviation is greater than zero, involves backtracking the parameter adjustment process to update the feedback response model and determine the correction vector, including: The attractiveness balance index was used to assess content style adaptability and obtain style bias. If the style deviation is greater than zero, the process will backtrack to the parameter adjustment stage to update the feedback response model. Determine the correction vector from the updated feedback response model; The adjusted parameter set is obtained by modifying the content of the vector and generating parameters. Generate content style feature vectors based on the adjusted parameter set; A style matching score is obtained by calculating the matching degree based on the content style feature vector and user preferences. Content items are sorted by style matching score to form a revised push sequence; The attractiveness balance index is recalculated by statistically analyzing the content distribution ratio in the revised push sequence and combining it with the revision vector.

9. The intelligent investment promotion management method for industrial parks based on machine learning according to claim 1, characterized in that, The step of extracting the interaction items between policy incentives and chart responses from the correction vector, and refining the tracking path of preference changes through these interaction items to obtain a continuously optimized push mechanism, includes: The interaction factors between policy incentives and chart responses are extracted from the correction vector, and the extracted interaction factors are classified to obtain a preliminary set of preference influences. Based on the initial set of preference influences, we analyze the potential trends of preference changes and determine the path records of preference changes by constructing a data structure to track the routes. Based on the path records of preference changes, a pre-established response pattern is used to dynamically update the output content of the push mechanism and obtain the adjusted content distribution strategy. Based on the adjusted content distribution strategy and the user feedback data stream, if the feedback data indicates a shift in preferences, a continuous optimization process is triggered to determine the direction of optimization. By optimizing the direction and updating the internal parameters of the push mechanism, a new content push sequence is obtained based on the dynamically updated parameter configuration. Based on the new content push sequence, analyze the matching degree between interaction factors and response patterns, and determine the final content distribution scheme based on the matching degree results; The final content distribution scheme is adopted, combined with continuously optimized data loops, to obtain real-time user feedback information and complete the response adjustment of the push mechanism.

10. A machine learning-based intelligent investment promotion management system for industrial parks, characterized in that: The system includes: The feedback response indicator extraction module is used to extract feedback response indicators from click-through rate and dwell time by collecting enterprise interaction log records, and obtain attribute parameter vectors based on the initial weights of professional depth and data proportion according to the indicators. The reinforcement learning processing module is used to process the attribute parameter vector using a reinforcement learning algorithm. If the feedback response index exceeds a preset threshold, the correlation strength between policy incentives and case similarity is increased, and the adjusted parameter vector is obtained through iterative updates. The optimized content template determination module is used to analyze the constraint relationship between abstract description and sentiment based on the adjusted parameter vector. If the proportion of abstract description is higher than the proportion of data, its proportion is reduced and chart response elements are integrated to determine the optimized content template. The balance constraint matrix calculation module is used to obtain the preference change signal in the optimization content template, and calculate the gradient difference between professional depth and policy incentives through the gradient descent method for the parameter adjustment process to obtain the balance constraint matrix; The final attribute weight set determination module is used to determine the matching degree of case similarity and sentiment tendency from the balance constraint matrix. If the matching degree is lower than the preset threshold, feedback response data is injected to reconstruct the matrix and determine the final attribute weight set. The attractiveness balance index generation module is used to generate a targeted content push sequence through the final attribute weight set, and obtain the attractiveness balance index based on the data proportion and dynamic ratio of the abstract description in the sequence. The feedback response model correction module is used to verify the adaptability of the content style using the attractiveness balance index. If the style deviation is greater than zero, the feedback response model is updated by backtracking the parameter adjustment step to determine the correction vector. The continuous optimization push mechanism generation module is used to extract the interaction items between policy incentives and chart responses from the correction vector, and to refine the tracking path of preference changes through the interaction items to obtain the continuous optimization push mechanism.

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