A method for extracting hot events based on the Internet

Abstract

The present invention relates to the field of data analysis technology, and discloses a method for extracting hot events based on the Internet, comprising: a method for extracting hot events based on the Internet, by collecting and sorting the keywords and the number of queries when users query, selecting target keywords and forming a user set. Then, selecting users in the user set, obtaining the hot events they have browsed, and calculating the average browsing rate, determining the hot events and counting the actual number of browsing times. According to the actual number of browsing times, the keywords are divided into two groups: high-frequency and low-frequency. For low-frequency keywords, the average growth rate is calculated by setting a sub-time interval, and a growth threshold is set, and the low-frequency keywords that meet the conditions are moved into the high-frequency set. Finally, the high-frequency keyword set is sorted according to the comprehensive score to indicate the popularity of different events, thereby achieving effective extraction and sorting of hot events on the Internet.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method for extracting popular events based on the Internet. Background Art

[0002] With the rapid development of the internet and the increasing convenience of information dissemination, the internet has become the primary channel for people to obtain information, communicate and interact. Every day, a vast amount of information is generated and disseminated online. Among these, trending events, as the focus of social attention, spark widespread discussion and participation.

[0003] However, from this vast ocean of information, quickly and accurately extracting truly trending events presents a significant challenge. Traditional methods for extracting trending events often rely on manual editing or simple statistical indicators, which struggle to adapt to the rapidly changing and diverse nature of online information, leading to issues such as information overload and delayed hot topics. Therefore, developing a method that can dynamically and accurately extract trending internet events in real time is crucial for understanding social dynamics, understanding public opinion trends, and assisting decision-making.

[0004] Existing methods for extracting popular events have the following limitations. First, traditional methods often use simple keyword statistics, such as keyword search frequency and number of clicks, as indicators to measure event popularity, ignoring the user's interest in the event and browsing behavior, which can easily lead to some low-attention events being misjudged as hot spots. Second, traditional methods have difficulty capturing the dynamic changes in event popularity. Internet hot spots are often time-sensitive. An event may erupt rapidly in a short period of time and then quickly subside. Traditional static analysis methods find it difficult to capture such dynamic changes. In addition, traditional methods often use manual screening and editing, which is inefficient and cannot adapt to the rapid updating of Internet information. Furthermore, many methods do not fully utilize user behavior data, and it is difficult to assess the user's true interest in the event based solely on the number of clicks.

[0005] To address these issues, this technical solution proposes a method for extracting popular events based on the internet. By analyzing user query and browsing behavior, as well as the dynamic changes in events themselves, this method can more accurately extract truly popular events, possessing significant theoretical significance and practical application value. This method effectively addresses many of the current challenges in extracting popular events, providing strong support for timely understanding of social dynamics, understanding public opinion trends, and assisting decision-making. Summary of the Invention

[0006] The present invention provides a method for extracting hot events based on the Internet, which is used to promote the solution of the problems mentioned in the above background technology.

[0007] The present invention provides the following technical solution: a method for extracting hot events based on the Internet, comprising:

[0008] S1. Collect keywords from all users during query, record the number of queries for each keyword, sort by the number of queries, select the sorted keywords as target keywords, and obtain users who query the target keywords to form a user set;

[0009] S2. Select any user in the user set, obtain the popular events that the user has browsed, calculate their average browsing rate, and determine the popular events based on the user's average browsing rate and the total duration of the events corresponding to the target keyword. Count the actual number of views of each popular event corresponding to the target keyword;

[0010] S3. Calculate the average number of views of the keywords based on the actual number of views, and enter the keywords whose actual number of views is greater than or equal to the average number of views into the first keyword set, and enter the keywords whose actual number of views is less than the average number of views into the second keyword set;

[0011] S4. Set multiple sub-time intervals, select keywords from the second keyword set as low-frequency keywords in sequence, and calculate the average growth rate based on the growth rate of the actual number of views in each sub-time interval;

[0012] S5. Set a growth threshold, move low-frequency keywords with an average growth rate greater than or equal to the growth threshold into the first set, and sort the first keyword set according to the comprehensive score to indicate the popularity of different events.

[0013] Optionally, the method collects keywords used by all users in their queries, records the number of queries for each keyword, sorts them by number of queries, selects the sorted keywords as target keywords in turn, and obtains users who query the target keywords to form a user set, including:

[0014] Set the time interval for collecting keywords;

[0015] Get the keywords used by all users when searching within a certain time interval;

[0016] Each keyword corresponds to multiple events, and all events are presented in the form of videos;

[0017] Record the number of queries for each keyword separately and enter the frequency set;

[0018] Sort the keywords in the frequency set from largest to smallest according to the number of queries;

[0019] Select keywords in the frequency set as target keywords in sequence;

[0020] During the time interval, obtain the keywords of each user query. If the keyword queried by the user is the target keyword, enter the user into the user set.

[0021] Each query corresponds to a user.

[0022] Optionally, selecting any user from the user set, obtaining the popular events browsed by the user, calculating the average browsing rate, determining the popular events based on the user's average browsing rate and the total duration of the events corresponding to the target keyword, and counting the actual number of views of the popular events corresponding to each target keyword include:

[0023] Select any user in the user set as the target user;

[0024] Obtain the target user's consent, obtain all popular events that the target user has browsed in the past, and record them in the browsing collection;

[0025] The average browsing rate of target users is calculated using the following formula:

[0026]

[0027] In the above formula, R is the average browsing rate of the target user in the browsing set, n is the total number of elements in the browsing set, g is i is the actual time the target user spends viewing the i-th popular event in the browsing set, t i is the total duration of browsing the i-th popular event in the collection;

[0028] Get all events corresponding to the target keyword, determine the hot events of the target keyword based on the actual time all users browsed each event and the total duration of the corresponding events, and get the actual number of views of the hot events corresponding to the target keyword.

[0029] Optionally, obtaining all events corresponding to the target keyword, determining the hot events for the target keyword based on the actual time when all users browsed each event and the total duration of the corresponding events, and obtaining the actual number of views of the hot events corresponding to the target keyword include:

[0030] Obtain all events corresponding to target keywords browsed by target users and record them into an event collection;

[0031] Calculate the product of the target user's average browsing rate and the total duration of each event in the event set;

[0032] Mark the browsing results that are greater than or equal to the actual time of the corresponding event as irrelevant;

[0033] Reselect any user in the user set as the new target user and calculate the product of the actual time the target user browses all events corresponding to the target keyword and the average browsing rate of the target user until all users in the user set are traversed.

[0034] Optionally, obtaining all events corresponding to the target keyword, determining the hot events for the target keyword based on the actual time when all users browsed each event and the total duration of the corresponding events, and obtaining the actual number of views of the hot events corresponding to the target keyword include:

[0035] Mark the browsing times when the product is less than the actual time of the corresponding event as relevant browsing, and obtain the time when the relevant browsing occurs;

[0036] Reselect any user in the user set as a new target user and calculate the product of the actual time the target user browses all events corresponding to the target keyword and the average browsing rate of the target user;

[0037] When all users in the user set are traversed, the event with the most relevant views in the event set is obtained as the hot event of the target keyword;

[0038] The number of relevant views of the target keyword corresponding to the hot event is recorded as the actual number of views;

[0039] Reselect any keyword in the frequency set as a new target keyword, and obtain the actual number of views of the hot event corresponding to the target keyword, until all keywords in the frequency set are traversed.

[0040] Optionally, the step of calculating the average number of views of a keyword based on the actual number of views, and entering keywords with actual number of views greater than or equal to the average number of views into a first keyword set, and entering keywords with actual number of views less than the average number of views into a second keyword set, includes:

[0041] If all keywords in the frequency set are traversed, the average number of views of the frequency set is calculated;

[0042] Average number of views = the sum of the actual number of views of all keywords in the frequency set / the number of keywords in the frequency set;

[0043] Enter keywords in the frequency set whose actual browsing times are greater than or equal to the average browsing times into the first keyword set;

[0044] Keywords in the frequency set whose actual browsing times are less than the average browsing times are entered into the second keyword set.

[0045] Optionally, setting multiple sub-time intervals, sequentially selecting keywords in the second keyword set as low-frequency keywords, and calculating an average growth rate based on the growth rate of their actual browsing times in each sub-time interval includes:

[0046] Divide the time interval into multiple sub-time intervals of equal length;

[0047] Select any keyword in the second keyword set as a low-frequency keyword;

[0048] Obtain the number of low-frequency keyword-related views in each sub-time interval in order from morning to night, and record them in a trend set;

[0049] The average growth rate of low-frequency keywords is calculated using the following formula:

[0050]

[0051] In the above formula, Z is the average growth rate of low-frequency keywords, m is the number of elements in the trend set, and a i+1 is the i+1th element in the trend set corresponding to the low-frequency keyword, a i is the i-th element in the trend set corresponding to the low-frequency keyword.

[0052] Optionally, the step of setting a growth threshold, moving low-frequency keywords with an average growth rate greater than or equal to the growth threshold into the first set, and sorting the first keyword set according to the comprehensive score to indicate the popularity of different events, includes:

[0053] Set the growth threshold used to determine the average growth rate;

[0054] If the average growth rate of the low-frequency keyword is greater than or equal to the growth threshold, the low-frequency keyword is entered into the first keyword set, and any element in the second keyword set is reselected as a new low-frequency keyword, and the average growth rate of the low-frequency keyword is calculated;

[0055] If the average growth rate of the low-frequency keyword is less than the growth threshold, the low-frequency keyword is removed from the second keyword set, and any element in the second keyword set is reselected as a new low-frequency keyword, and the average growth rate of the low-frequency keyword is calculated;

[0056] When all keywords in the second keyword set are traversed, the formula Calculate the comprehensive score of each keyword in the first keyword set, where V is the actual number of page views of the keyword and Z is the average growth rate of the keyword;

[0057] Sort the popular events corresponding to all the keywords in the first keyword set from largest to smallest according to the comprehensive scores of the keywords.

[0058] The present invention has the following beneficial effects:

[0059] 1. This internet-based method for extracting popular events uses a set time interval to collect keywords searched by users within that time interval. All keywords searched by users within that time interval are recorded. Each keyword corresponds to multiple events presented in video format. The system then counts the number of times each keyword is searched and records the keyword and its corresponding search count in a frequency set. Keywords in the frequency set are sorted from highest to lowest by search count. The system then sequentially selects keywords from the frequency set as target keywords. For each target keyword, the system searches for users who have searched for that keyword within the time interval and adds these users to the user set corresponding to the target keyword. It is important to note that each query corresponds to a single user; this effectively filters out high-frequency keywords and clusters users who have searched for these keywords, providing a data foundation for subsequent analysis of popular events. Sorting keywords by search count prioritizes keywords with high user interest, improving analysis efficiency. Recording the users associated with each keyword facilitates subsequent analysis of user browsing behavior and assesses their genuine interest in events related to that keyword. Events presented in video format make it easier to collect user behavior data. This method can quickly identify user groups associated with specific keywords, providing strong support for subsequent precise analysis and hot event mining.

[0060] 2. This internet-based method for extracting popular events selects a target user from a user set as the target user. With the user's consent, all popular events previously viewed by the user are retrieved and entered into a browsed set. Next, the target user's average browse rate is calculated using a formula that divides the actual viewing time of each event by the total viewing time of the event and calculates the average. Based on the actual viewing time and total viewing time of events corresponding to the target keyword by all users, popular events for the target keyword are preliminarily identified, and the actual number of views of these popular events is counted. This method quantifies the user's average browsing habits based on their historical browsing behavior, providing a benchmark for subsequently assessing the user's attention to specific events. The calculation of the average browse rate considers the relationship between the actual viewing time and total viewing time of each event, enabling a more accurate assessment of the user's browsing depth and interest. Preliminary identification of popular events for the target keyword based on the browsing data of all users provides guidance for subsequent hot event screening. This method leverages user historical behavior data to more closely reflect the user's actual interests and preferences, providing a more reliable foundation for subsequent hot event extraction, ensuring that the extracted hot events are more aligned with the user's actual focus.

[0061] 3. This internet-based method for extracting popular events involves obtaining all events corresponding to a target keyword that the user has browsed for each target user and placing them into an event collection. The method then calculates the product of the user's average browsing rate and the total duration of each event in the event collection, and compares this product with the actual time the user browsed the corresponding event. If the product is greater than or equal to the actual browsing time, the browsing is considered irrelevant; conversely, if the product is less than the actual browsing time, the browsing is considered relevant, and the time of the relevant browsing is recorded. This process iterates across all users in the user collection, performing the same calculation for each user. After iterating over all users, the system counts the number of relevant browsings for all events in the event collection and identifies the event with the most relevant browsings as the popular event for the target keyword. Finally, the number of relevant browsings for the popular event corresponding to the target keyword is recorded as the actual number of browsings for the popular event, and this process is repeated for all target keywords. This effectively filters out events that users may have accidentally or quickly browsed, retaining those that they are truly interested in and have browsed in depth, thereby more accurately assessing the popularity of events. By incorporating the average user browsing rate as a reference, we can more accurately determine whether users have actively viewed an event, rather than relying solely on the number of clicks. By categorizing browsing behavior into relevant and irrelevant browsing and selecting the most relevant events as trending events, we can improve the accuracy of trending event screening. Furthermore, counting the number of relevant views of trending events provides a more reliable data foundation for subsequent evaluation of keyword popularity, making subsequent trending event extraction more accurate and efficient.

[0062] 4. This internet-based method for extracting popular events. After traversing all keywords in a frequency set, the system calculates the average number of views for all keywords in the frequency set. This calculation is done by dividing the sum of the actual number of views for all keywords in the frequency set by the number of keywords in the frequency set. The system then divides the keywords into two categories based on the comparison of the actual number of views with the average number of views. Keywords with actual number of views greater than or equal to the average number of views are entered into the first keyword set, while keywords with actual number of views less than the average number of views are entered into the second keyword set. This method effectively categorizes keywords based on their popularity, providing a foundation for subsequent keyword analysis of different types. By calculating the average number of views, it is possible to quickly determine which keywords have high attention, allowing these high-frequency keywords to be prioritized, improving analysis efficiency. Keyword groups with number of views less than the average number of views into the second set enable subsequent screening of potential popular events based on growth rate, avoiding the omission of potentially catching up. This classification method lays the foundation for subsequent dynamic analysis, particularly the growth trend analysis of low-frequency keywords.

[0063] 5. This internet-based method for extracting popular events involves dividing a time interval into multiple sub-time intervals of equal length. A keyword is then selected from a second keyword set as a low-frequency keyword. The number of views of the low-frequency keyword within each sub-time interval is then counted in chronological order and recorded in a trend set. The average growth rate of the low-frequency keyword is then calculated using a formula that averages the growth rates of two adjacent elements in the trend set. A growth threshold is set. If the average growth rate of a low-frequency keyword is greater than or equal to the threshold, the keyword is moved from the second set to the first set; if it is less than the threshold, the keyword is removed from the second set. These steps are repeated until all keywords in the second set have been traversed. Finally, a comprehensive scoring formula is used to mark all events corresponding to the keywords in the first set as popular events. These events are then sorted by their comprehensive scores to indicate the popularity of events during different time periods. This method effectively identifies potential hot events that may be developing, addressing the shortcomings of traditional methods that focus solely on high-frequency keywords. By analyzing the growth rate of low-frequency keywords, we can promptly identify events that, despite low current page views, are rapidly growing, thereby providing a more comprehensive assessment of event popularity. Using sub-time intervals allows for more granular capture of event dynamics. Furthermore, the average growth rate metric allows for a quantitative assessment of event growth trends, avoiding errors caused by subjective judgment. Finally, using a comprehensive score as the basis for ranking more comprehensively considers the impact of page views and growth rate, resulting in a more rational ranking of popular events that better reflects user interest. This dynamic analysis method enables the timely identification of potential hot events and more accurately assesses their popularity, providing users with more comprehensive and timely information services. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0066] Example 1, see Figure 1 , a method for extracting hot events based on the Internet, comprising:

[0067] S1. Collect keywords from all users during query, record the number of queries for each keyword, sort by the number of queries, select the sorted keywords as target keywords, and obtain users who query the target keywords to form a user set;

[0068] S2. Select any user in the user set, obtain the popular events that the user has browsed, calculate their average browsing rate, and determine the popular events based on the user's average browsing rate and the total duration of the events corresponding to the target keyword. Count the actual number of views of each popular event corresponding to the target keyword;

[0069] S3. Calculate the average number of views of the keywords based on the actual number of views, and enter the keywords whose actual number of views is greater than or equal to the average number of views into the first keyword set, and enter the keywords whose actual number of views is less than the average number of views into the second keyword set;

[0070] S4. Set multiple sub-time intervals, select keywords from the second keyword set as low-frequency keywords in sequence, and calculate the average growth rate based on the growth rate of the actual number of views in each sub-time interval;

[0071] S5. Set a growth threshold, move low-frequency keywords with an average growth rate greater than or equal to the growth threshold into the first set, and sort the first keyword set according to the comprehensive score to indicate the popularity of different events.

[0072] The method collects keywords from all users during query, records the number of queries for each keyword, and sorts them by the number of queries. The sorted keywords are selected as target keywords in turn, and the users who query the target keywords are obtained to form a user set, including:

[0073] Set the time interval for collecting keywords;

[0074] Get the keywords used by all users when searching within a certain time interval;

[0075] Each keyword corresponds to multiple events, and all events are presented in the form of videos;

[0076] Record the number of queries for each keyword separately and enter the frequency set;

[0077] Sort the keywords in the frequency set from largest to smallest according to the number of queries;

[0078] Select keywords in the frequency set as target keywords in sequence;

[0079] During the time interval, obtain the keywords of each user query. If the keyword queried by the user is the target keyword, enter the user into the user set.

[0080] Each query corresponds to a user.

[0081] A time interval is set to collect keyword searches performed by users during that time interval. All keyword searches performed by users during that time interval are recorded. Each keyword corresponds to multiple events presented in video format. The system then counts the number of searches for each keyword and records the keyword and its corresponding search count in a frequency set. Keywords in the frequency set are sorted from highest to lowest by search count. The system then selects keywords from the frequency set as target keywords. For each target keyword, the system searches for users who searched for that keyword during the time interval and adds these users to the user set corresponding to the target keyword. It is important to note that each search is associated with a single user; this effectively identifies high-frequency keywords and clusters users who have searched for them, providing a data foundation for subsequent analysis of popular events. Sorting keywords by search count prioritizes keywords with high user interest, improving analysis efficiency. Recording the users associated with each keyword facilitates subsequent analysis of user browsing behavior and assesses their genuine interest in events related to that keyword. Events presented in video format make it easier to collect user behavior data. This method can quickly find user groups associated with specific keywords, providing strong support for subsequent precise analysis and hot event mining.

[0082] The method of selecting any user from the user set, obtaining the popular events browsed by the user, calculating the average browsing rate, determining the popular events based on the average browsing rate of the user and the total duration of the events corresponding to the target keyword, and counting the actual number of views of the popular events corresponding to each target keyword includes:

[0083] Select any user in the user set as the target user;

[0084] Obtain the target user's consent, obtain all popular events that the target user has browsed in the past, and record them in the browsing collection;

[0085] The average browsing rate of target users is calculated using the following formula:

[0086]

[0087] In the above formula, R is the average browsing rate of the target user in the browsing set, n is the total number of elements in the browsing set, g is i is the actual time the target user spends viewing the i-th popular event in the browsing set, t i is the total duration of browsing the i-th popular event in the collection;

[0088] Get all events corresponding to the target keyword, determine the hot events of the target keyword based on the actual time all users browsed each event and the total duration of the corresponding events, and get the actual number of views of the hot events corresponding to the target keyword.

[0089] This method selects a target user from a user collection as the target user. With the user's consent, all previously viewed popular events are retrieved and entered into a browsed collection. Next, the target user's average browse rate is calculated using a formula that divides the user's actual viewing time for each event by the total viewing time for that event and calculates the average. Based on the actual viewing time and total viewing time of all users for events corresponding to the target keyword, the popular events for the target keyword are preliminarily identified and the actual number of views for these popular events is counted. This method quantifies the user's average browsing habits based on their historical browsing behavior, providing a benchmark for subsequently assessing their attention to specific events. The calculation of the average browse rate considers the relationship between the actual viewing time and total viewing time of each event, enabling a more accurate assessment of user browsing depth and interest. Preliminary identification of popular events for the target keyword based on all users' browsing data provides guidance for subsequent hot event screening. This method leverages user historical behavior data to more closely reflect users' actual interests and preferences, providing a more reliable foundation for subsequent hot event extraction, ensuring that the extracted hot events are more aligned with users' actual interests.

[0090] The step of obtaining all events corresponding to the target keyword, determining the hot events of the target keyword based on the actual time when all users browsed each event and the total duration of the corresponding events, and obtaining the actual number of views of the hot events corresponding to the target keyword includes:

[0091] Obtain all events corresponding to target keywords browsed by target users and record them into an event collection;

[0092] Calculate the product of the target user's average browsing rate and the total duration of each event in the event set;

[0093] Mark the browsing results that are greater than or equal to the actual time of the corresponding event as irrelevant;

[0094] Reselect any user in the user set as the new target user and calculate the product of the actual time the target user browses all events corresponding to the target keyword and the average browsing rate of the target user until all users in the user set are traversed.

[0095] The step of obtaining all events corresponding to the target keyword, determining the hot events of the target keyword based on the actual time when all users browsed each event and the total duration of the corresponding events, and obtaining the actual number of views of the hot events corresponding to the target keyword includes:

[0096] Mark the browsing times when the product is less than the actual time of the corresponding event as relevant browsing, and obtain the time when the relevant browsing occurs;

[0097] Reselect any user in the user set as a new target user and calculate the product of the actual time the target user browses all events corresponding to the target keyword and the average browsing rate of the target user;

[0098] When all users in the user set are traversed, the event with the most relevant views in the event set is obtained as the hot event of the target keyword;

[0099] The number of relevant views of the target keyword corresponding to the hot event is recorded as the actual number of views;

[0100] Reselect any keyword in the frequency set as a new target keyword, and obtain the actual number of views of the hot event corresponding to the target keyword, until all keywords in the frequency set are traversed.

[0101] For each target user, all events corresponding to the target keyword that the user has viewed are retrieved and placed into an event collection. Next, the user's average view rate is multiplied by the total duration of each event in the event collection, and this product is compared with the actual time the user viewed the corresponding event. If the product is greater than or equal to the actual view time, the view is considered irrelevant; conversely, if the product is less than the actual view time, the view is considered relevant, and the time of the relevant view is recorded. This process is repeated for all users in the user collection, with the same calculation performed for each user. After all users have been traversed, the system counts the number of relevant views of all events in the event collection and identifies the event with the most relevant views as the hot event for the target keyword. Finally, the number of relevant views of the hot event corresponding to the target keyword is recorded as the actual number of views for the hot event, and this process is repeated for all target keywords. This effectively filters out events that users may have accidentally or quickly viewed, retaining those that they are truly interested in and have viewed in depth, thereby more accurately assessing the popularity of events. By using the user's average view rate as a reference, it is possible to more accurately determine whether a user has viewed an event carefully, rather than relying solely on the number of clicks. By classifying browsing behavior into relevant and irrelevant browsing and selecting the most relevant browsing events as hot events, we can improve the accuracy of hot event screening. At the same time, counting the number of relevant browsing times for hot events provides a more reliable data foundation for subsequent evaluation of keyword popularity, making subsequent hot event extraction more accurate and effective.

[0102] The method of calculating the average number of views of keywords based on the actual number of views, and entering keywords with actual number of views greater than or equal to the average number of views into the first keyword set, and entering keywords with actual number of views less than the average number of views into the second keyword set, includes:

[0103] If all keywords in the frequency set are traversed, the average number of views of the frequency set is calculated;

[0104] Average number of views = the sum of the actual number of views of all keywords in the frequency set / the number of keywords in the frequency set;

[0105] Enter keywords in the frequency set whose actual browsing times are greater than or equal to the average browsing times into the first keyword set;

[0106] Keywords in the frequency set whose actual browsing times are less than the average browsing times are entered into the second keyword set.

[0107] After the system has traversed all keywords in the frequency set, it calculates the average number of views for all keywords in the frequency set. This calculation is done by dividing the sum of the actual view counts for all keywords in the frequency set by the number of keywords in the frequency set. The system then divides the keywords into two categories based on the comparison of the actual view counts with the average view counts. Keywords with actual view counts greater than or equal to the average view count are entered into the first keyword set, while keywords with actual view counts less than the average view count are entered into the second keyword set. This effectively categorizes keywords based on their popularity, providing a foundation for subsequent keyword analysis of different types. By calculating the average view count, it is possible to quickly identify keywords with high attention, prioritizing these high-frequency keywords and improving analysis efficiency. Assigning keywords with view counts less than the average to the second set allows for subsequent screening of potential hot events based on growth rates, avoiding the omission of potentially catching up. This categorization lays the foundation for subsequent dynamic analysis, particularly the growth trend analysis of low-frequency keywords.

[0108] The step of setting multiple sub-time intervals, sequentially selecting keywords from the second keyword set as low-frequency keywords, and calculating an average growth rate based on the growth rate of the actual number of views in each sub-time interval includes:

[0109] Divide the time interval into multiple sub-time intervals of equal length;

[0110] Select any keyword in the second keyword set as a low-frequency keyword;

[0111] Obtain the number of low-frequency keyword-related views in each sub-time interval in order from morning to night, and record them in a trend set;

[0112] The average growth rate of low-frequency keywords is calculated using the following formula:

[0113]

[0114] In the above formula, Z is the average growth rate of low-frequency keywords, m is the number of elements in the trend set, and a i+1 is the i+1th element in the trend set corresponding to the low-frequency keyword, a i is the i-th element in the trend set corresponding to the low-frequency keyword.

[0115] The method of setting a growth threshold, moving low-frequency keywords with an average growth rate greater than or equal to the growth threshold into the first set, and sorting the first keyword set according to the comprehensive score to indicate the popularity of different events includes:

[0116] Set the growth threshold used to determine the average growth rate;

[0117] If the average growth rate of the low-frequency keyword is greater than or equal to the growth threshold, the low-frequency keyword is entered into the first keyword set, and any element in the second keyword set is reselected as a new low-frequency keyword, and the average growth rate of the low-frequency keyword is calculated;

[0118] If the average growth rate of the low-frequency keyword is less than the growth threshold, the low-frequency keyword is removed from the second keyword set, and any element in the second keyword set is reselected as a new low-frequency keyword, and the average growth rate of the low-frequency keyword is calculated;

[0119] When all keywords in the second keyword set are traversed, the formula Calculate the comprehensive score of each keyword in the first keyword set, where V is the actual number of page views of the keyword and Z is the average growth rate of the keyword;

[0120] Sort the popular events corresponding to all the keywords in the first keyword set from largest to smallest according to the comprehensive scores of the keywords.

[0121] The entire time interval is divided into multiple sub-intervals of equal length. A keyword is then selected from the second keyword set as a low-frequency keyword. The number of page views of the low-frequency keyword in each sub-interval is then counted in chronological order and recorded in a trend set. The average growth rate of the low-frequency keyword is then calculated using a formula that averages the growth rates of two adjacent elements in the trend set. A growth threshold is set. If the average growth rate of a low-frequency keyword is greater than or equal to the threshold, the keyword is moved from the second set to the first set; if it is less than the threshold, the keyword is removed from the second set. This process is repeated until all keywords in the second set have been traversed. Finally, a comprehensive scoring formula is used to mark all events corresponding to the keywords in the first set as hot events. These events are then sorted by their comprehensive scores to indicate the popularity of events during different time periods. This method effectively identifies potential hot events that may be emerging, overcoming the shortcomings of traditional methods that focus solely on high-frequency keywords. By analyzing the growth rate of low-frequency keywords, events with low current page views but rapid growth rates can be identified, providing a more comprehensive assessment of event popularity. Using sub-time intervals allows for more precise capture of event dynamics. Furthermore, the average growth rate metric allows for quantitative assessment of event growth trends, thus avoiding errors caused by subjective judgment. Finally, using a comprehensive score as the basis for ranking more comprehensively considers the impact of pageviews and growth rate, resulting in a more rational ranking of popular events that better reflects user interest. This dynamic analysis method enables the timely identification of potential hot events and more accurately assesses their popularity, providing users with more comprehensive and timely information services.

[0122] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0123] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for extracting hot events based on the Internet, characterized in that: include: S1. Collect keywords from all users during query, record the number of queries for each keyword, sort by the number of queries, select the sorted keywords as target keywords, and obtain users who query the target keywords to form a user set; S2. Select any user in the user set, obtain the popular events that the user has browsed, calculate the average browsing rate, and determine the popular events based on the user's average browsing rate and the total duration of the events corresponding to the target keyword. Count the actual number of views of each popular event corresponding to the target keyword, specifically including: Select any user in the user set as the target user; Obtain the target user's consent, obtain all popular events that the target user has browsed in the past, and record them in the browsing collection; The average browsing rate of target users is calculated using the following formula: , where R is the average browsing rate of the target user in the browsing set, n is the total number of elements in the browsing set, The actual time the target user spends viewing the i-th popular event in the browsing set, is the total duration of browsing the i-th popular event in the collection; Obtain all events corresponding to the target keyword, determine the hot events for the target keyword based on the actual time all users browsed each event and the total duration of the corresponding events, and obtain the actual number of views of the hot events corresponding to the target keyword; Obtain all events corresponding to target keywords browsed by target users and record them into an event collection; Calculate the product of the target user's average browsing rate and the total duration of each event in the event set; Mark the browsing results that are greater than or equal to the actual time of the corresponding event as irrelevant; Reselect any user in the user set as the new target user and calculate the product of the actual time the target user browses all events corresponding to the target keyword and the average browsing rate of the target user until all users in the user set are traversed; Mark the browsing times when the product is less than the actual time of the corresponding event as relevant browsing, and obtain the time when the relevant browsing occurs; Reselect any user in the user set as a new target user and calculate the product of the actual time the target user browses all events corresponding to the target keyword and the average browsing rate of the target user; When all users in the user set are traversed, the event with the most relevant views in the event set is obtained as the hot event of the target keyword; The number of relevant views of the target keyword corresponding to the hot event is recorded as the actual number of views; Reselect any keyword in the frequency set as a new target keyword, and obtain the actual number of views of the hot event corresponding to the target keyword until all keywords in the frequency set are traversed; S3. Calculate the average number of views of the keywords based on the actual number of views, and enter the keywords whose actual number of views is greater than or equal to the average number of views into the first keyword set, and enter the keywords whose actual number of views is less than the average number of views into the second keyword set; S4. Set multiple sub-time intervals, select keywords from the second keyword set as low-frequency keywords in sequence, and calculate the average growth rate based on the growth rate of the actual number of views in each sub-time interval; S5. Set a growth threshold, move low-frequency keywords with an average growth rate greater than or equal to the growth threshold into the first set, and sort the first keyword set according to the comprehensive score to indicate the popularity of different events.

2. The method for extracting hot events based on the Internet according to claim 1, wherein: The method collects keywords from all users during query, records the number of queries for each keyword, and sorts them by the number of queries. The sorted keywords are selected as target keywords in turn, and the users who query the target keywords are obtained to form a user set, including: Set the time interval for collecting keywords; Get the keywords used by all users when searching within a certain time interval; Each keyword corresponds to multiple events, and all events are presented in the form of videos; Record the number of queries for each keyword separately and enter the frequency set; Sort the keywords in the frequency set from largest to smallest according to the number of queries; Select keywords in the frequency set as target keywords in sequence; During the time interval, obtain the keywords of each user query. If the keyword queried by the user is the target keyword, enter the user into the user set. Each query corresponds to a user.

3. The method for extracting hot events based on the Internet according to claim 1, wherein: The method of calculating the average number of views of keywords based on the actual number of views, and entering keywords with actual number of views greater than or equal to the average number of views into the first keyword set, and entering keywords with actual number of views less than the average number of views into the second keyword set, includes: If all keywords in the frequency set are traversed, the average number of views of the frequency set is calculated; Average number of views = the sum of the actual number of views of all keywords in the frequency set ÷ the number of keywords in the frequency set; Enter keywords in the frequency set whose actual browsing times are greater than or equal to the average browsing times into the first keyword set; Keywords in the frequency set whose actual browsing times are less than the average browsing times are entered into the second keyword set.

4. The method for extracting hot events based on the Internet according to claim 1, wherein: The step of setting multiple sub-time intervals, sequentially selecting keywords from the second keyword set as low-frequency keywords, and calculating an average growth rate based on the growth rate of the actual number of views in each sub-time interval includes: Divide the time interval into multiple sub-time intervals of equal length; Select any keyword in the second keyword set as a low-frequency keyword; Obtain the number of low-frequency keyword-related views in each sub-time interval in order from morning to night, and record them in a trend set; The average growth rate of low-frequency keywords is calculated using the following formula: , where Z is the average growth rate of low-frequency keywords, m is the number of elements in the trend set, is the i+1th element in the trend set corresponding to the low-frequency keyword, is the i-th element in the trend set corresponding to the low-frequency keyword.

5. The method for extracting hot events based on the Internet according to claim 1, wherein: The method of setting a growth threshold, moving low-frequency keywords with an average growth rate greater than or equal to the growth threshold into the first set, and sorting the first keyword set according to the comprehensive score to indicate the popularity of different events includes: Set the growth threshold used to determine the average growth rate; If the average growth rate of the low-frequency keyword is greater than or equal to the growth threshold, the low-frequency keyword is entered into the first keyword set, and any element in the second keyword set is reselected as a new low-frequency keyword, and the average growth rate of the low-frequency keyword is calculated; If the average growth rate of the low-frequency keyword is less than the growth threshold, the low-frequency keyword is removed from the second keyword set, and any element in the second keyword set is reselected as a new low-frequency keyword, and the average growth rate of the low-frequency keyword is calculated; When all keywords in the second keyword set are traversed, the formula , calculate the comprehensive score of each keyword in the first keyword set, where V is the actual number of times the keyword is viewed, and Z is the average growth rate of the keyword; Sort the popular events corresponding to all the keywords in the first keyword set from largest to smallest according to the comprehensive scores of the keywords.

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